Wind power plant energy storage system for reducing wind power prediction deviation and control method

Abstract

The invention provides a wind power plant energy storage system for reducing wind power prediction deviation and a control method, the system comprises a plurality of energy storage devices, and each energy storage device comprises a battery system, an inverter and a circuit breaker; wherein each energy storage device is configured on a low-voltage alternating current side of a box-type transformer of the wind turbine generator at the tail end of the corresponding current collection wire; the battery system is connected with the first end of the inverter, the second end of the inverter is connected with the first end of the circuit breaker, and the second end of the circuit breaker is connected with the low-voltage alternating current side of the corresponding box-type transformer; and the energy storage device is used for carrying out corresponding charging and discharging operation based on a predicted deviation value when the actual power of the wind power plant exceeds the predicted deviation value of the predicted power and is greater than a preset deviation limit value, so as to adjust the actual power to be within an allowable predicted deviation range. According to the system, the energy storage device is controlled to charge and discharge, the wind power prediction deviation can be reduced, and the influence caused by the wind power prediction deviation is made up.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation prediction, and in particular to a wind farm energy storage system and a control method for reducing wind power prediction deviation. Background Art

[0002] With the increasing demand for clean energy, wind power generation, as a green and renewable energy, is gradually becoming an important part of the energy structure. Different from conventional energy, wind power has the characteristics of randomness, volatility and intermittency, and the output of wind power is unstable. With the large-scale grid connection of wind power generation, it will bring challenges to the dispatching and stable operation of the power grid. Therefore, the accurate prediction of wind power output has become an important part of the operation of wind farms.

[0003] Among them, according to the relevant regulations on power operation, the maximum error between the daily prediction curve of the wind farm and the actual result should not exceed the set maximum prediction deviation. The amount of electricity exceeding the maximum prediction deviation will cause a decrease in the operating efficiency of the wind farm.

[0004] In the related technologies, three types of methods are usually used for wind power prediction: physical methods, statistical methods, and combined methods. However, although a lot of research has been done on wind power prediction in the related technologies, and the prediction methods are constantly improving, the inherent random fluctuations of wind energy itself still lead to a large deviation between the predicted power and the actual results, which will result in waste of resources and other consequences.

[0005] Therefore, how to make up for the deviation between the predicted wind power generation power and the actual results has become a problem that needs to be solved urgently. Summary of the invention

[0006] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0007] To this end, the first purpose of the present application is to propose a wind farm energy storage system for reducing the deviation of wind power prediction. When the deviation between the predicted wind power and the actual result exceeds the limit, the system can perform corresponding charging and discharging through the energy storage device, thereby adjusting the deviation between the actual power generated by the wind farm and the predicted power within an allowable range and reducing the deviation of wind power prediction.

[0008] A second objective of the present application is to propose a control method for reducing wind power prediction deviation.

[0009] A third object of the present application is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above-mentioned purpose, the first aspect of the present application is to propose a wind farm energy storage system for reducing wind power prediction deviation, the system comprising: a plurality of energy storage devices, each of which corresponds to a collector line of a wind farm, each of which comprises: a battery system, an inverter and a circuit breaker; wherein,

[0011] Each of the energy storage devices is arranged at the low voltage AC side of the box transformer of the wind turbine generator set at the end of the corresponding collector line;

[0012] The battery system is connected to a first end of the inverter, the second end of the inverter is connected to a first end of the circuit breaker, and the second end of the circuit breaker is connected to a low-voltage AC side of the corresponding box-type transformer;

[0013] The energy storage device is used to perform charging and discharging operations when the deviation value of the actual power generated by the wind farm exceeds the predicted power by more than a preset deviation limit, so as to adjust the actual power generated to within an allowable predicted deviation range.

[0014] Optionally, in one embodiment of the present application, multiple wind turbines are configured on each of the collecting lines, and each of the wind turbines includes: a power generation system, a converter and the box-type transformer; each of the collecting lines is connected to the AC bus through a corresponding circuit breaker, and the AC bus is connected to a booster station, which is used to boost the AC bus voltage and then connect it to the power grid.

[0015] To achieve the above-mentioned purpose, the second aspect of the present application is to propose a control method for reducing wind power prediction deviation, which is applied to the wind farm energy storage system for reducing wind power prediction deviation described in the first aspect of the present application, and the method comprises the following steps:

[0016] Obtaining short-term forecast data of the wind power of the wind farm a day before, and dividing the control period of the wind farm output according to the forecast point interval in the short-term forecast data a day before;

[0017] Calculating the wind power prediction real-time deviation value corresponding to each control period according to the predicted power of the wind farm and the actual power generated by each collector line;

[0018] Obtaining a preset power forecast deviation limit value, and when the real-time deviation value corresponding to any control period is greater than the deviation limit value, calculating a forecast deviation value of the actual power generated by the wind farm exceeding an allowable forecast deviation range based on the real-time deviation value;

[0019] The power allocation value of each energy storage device in the wind farm is calculated based on the predicted deviation value, and each energy storage device is controlled to perform corresponding charging and discharging operations according to the power allocation value and the predicted deviation value, so as to adjust the actual power to within the allowable predicted deviation range.

[0020] Optionally, in one embodiment of the present application, the calculating the power allocation value of each energy storage device in the wind farm according to the predicted deviation value includes: calculating the charge and discharge limit coefficient of each energy storage device according to the charge state of each energy storage device, wherein the charge and discharge limit coefficient includes a discharge coefficient when the predicted deviation value is positive, and a charge coefficient when the predicted deviation value is negative; calculating the charge and discharge participation factor of each energy storage device according to the charge and discharge limit coefficient of each energy storage device; calculating the power allocation value of each energy storage device according to the charge and discharge participation factor of each energy storage device and the predicted deviation value.

[0021] Optionally, in one embodiment of the present application, after calculating the power allocation value of each energy storage device in the wind farm according to the predicted deviation value, it also includes: determining the charging and discharging target power corresponding to each energy storage device according to the maximum charging and discharging power of the energy storage device and the power allocation value of each energy storage device; and controlling each energy storage device to perform corresponding charging and discharging operations, including: based on a grid-based control strategy, controlling each energy storage device to track the corresponding target power.

[0022] Optionally, in one embodiment of the present application, the grid-based control strategy controls each of the energy storage devices to track the corresponding target power, including: obtaining multiple operating status parameters of the power grid, performing phase-locked loop phase angle calculation based on the multiple operating status parameters, and obtaining the power grid phase angle, wherein the multiple operating status parameters include the three-phase voltage of the power grid and the three-phase current output by the energy storage device; performing a current outer loop calculation based on the multiple operating status parameters and the target power to obtain the current control target value of the common connection point PCC of the energy storage device in the dq coordinate system; performing a current inner loop calculation based on the multiple operating status parameters, the power grid phase angle and the current control target value to obtain the inverter modulation voltage control target value of the energy storage device.

[0023] Optionally, in one embodiment of the present application, the phase-locked loop phase angle calculation based on the multiple operating status parameters includes: converting the three-phase voltage of the power grid into a d-axis power grid voltage and a q-axis power grid voltage in a dq coordinate system through an initial phase; performing PI calculation on the q-axis power grid voltage to obtain a power grid frequency, and calculating the power grid phase angle based on the power grid frequency.

[0024] Optionally, in one embodiment of the present application, the current outer loop calculation based on the multiple operating status parameters and the target power includes: converting the three-phase voltage and the three-phase current of the power grid into a voltage in a dq coordinate system and a current in a dq coordinate system, respectively; calculating the active power of the power grid and the reactive power of the power grid according to the voltage and the current in the dq coordinate system; using the target power as the active reference value, and calculating the d-axis current control target value and the q-axis current control target value of the common connection point PCC of the energy storage device according to the active power of the power grid, the reactive power of the power grid, the active reference value and the reactive reference value.

[0025] Optionally, in one embodiment of the present application, the current inner loop calculation is performed based on the operating state parameters, the grid phase angle and the current control target value, including: performing PI calculation on the current in the dq coordinate system and the current control target value in the dq coordinate system to obtain the voltage value corresponding to the current loop; combining the voltage value corresponding to the current loop, the voltage in the dq coordinate system and the dq axis cross-influence term to perform operations to obtain the inverter dq axis voltage control target value; using the grid phase angle, performing dq coordinate inverse conversion on the inverter dq axis voltage control target value to obtain the inverter modulation voltage control target value.

[0026] To achieve the above-mentioned objectives, the third aspect of the present invention proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the control method for reducing wind power prediction deviation as described in any one of the second aspects of the present application is implemented.

[0027] The technical solution provided by the embodiment of the present application brings at least the following beneficial effects: the present application configures an electrochemical energy storage device on the low-voltage AC side of the box transformer of the wind turbine at the end of each collector line in the wind farm, thereby realizing the distributed energy storage configuration of the wind farm. Furthermore, when the deviation value between the predicted wind power generation power and the actual result exceeds the limit value, the corresponding charging and discharging operation can be performed through the energy storage device based on the real-time deviation value of the predicted wind power generation power, so as to adjust the deviation between the actual power generation and the predicted power of the wind farm within the allowable range. The present application can effectively compensate for the impact of the excessive wind power prediction deviation, ensure that the actual power generation of the wind farm is within the allowable prediction deviation range, and achieve the effect of reducing the wind power prediction deviation and improving the wind power prediction accuracy. In addition, the present application can also reduce the number of distributed energy storage devices configured in the wind farm, without changing the existing wind farm networking form and wind turbine structure, thereby reducing the possible risks of the distributed energy storage system, reducing the cost of the energy storage system, and improving the stability and reliability of the wind farm operation.

[0028] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A schematic diagram of a wind farm equipment arrangement method proposed in an embodiment of the present application;

[0031] Figure 2 A flow chart of a control method for reducing wind power prediction deviation proposed in an embodiment of the present application;

[0032] Figure 3 A flowchart of a specific control method for reducing wind power prediction deviation proposed in an embodiment of the present application;

[0033] Figure 4 A schematic diagram of a real-time power prediction deviation proposed in an embodiment of the present application;

[0034] Figure 5 A flow chart of a target power following method based on a grid-following control strategy proposed in an embodiment of the present application;

[0035] Figure 6 A schematic diagram of the principle of a target power following method proposed in an embodiment of the present application;

[0036] Figure 7 A schematic diagram of the calculation principle of a power grid phase angle proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0038] It should be noted that wind power prediction in related technologies is mainly divided into three categories: physical methods, statistical methods and combined methods. Among them, the physical method is based on meteorology and the working principle of wind turbines. By establishing a mathematical model, it considers physical factors such as wind speed, wind direction, temperature, air pressure, topography, and wind turbine characteristics to predict power output. The statistical method is based on the analysis and research of historical statistical data and numerical weather forecast (NWP) data, establishes a mapping relationship between NWP data and wind farm output power, and mines the laws and trends in the data for prediction. The combined method combines physical methods and statistical methods to comprehensively utilize the advantages of both.

[0039] However, regardless of which of the above methods is used, the inherent random fluctuations of wind energy itself result in a large deviation between the predicted power and the actual results, which may lead to the waste of electricity resources and the frequent assessment and fines of wind farms.

[0040] To this end, the present application proposes a distributed energy storage configuration scheme in which electrochemical energy storage is configured on the low-voltage AC side of the wind turbine box at the end of each collector line, and a control method for improving wind power prediction accuracy, which can make up for the problem of excessive prediction deviation in traditional wind power prediction methods.

[0041] A wind farm energy storage system and a control method for reducing wind power prediction deviation proposed in an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Figure 1 A schematic diagram of a wind farm equipment layout proposed in an embodiment of the present application is shown in FIG. Figure 1 As shown, in the wind farm applied in the present application, the distributed energy storage system includes: a plurality of energy storage devices 4, each energy storage device corresponds to a collector line of the wind farm, and each energy storage device 4 includes: a battery system 4-1, an inverter 4-2 and a circuit breaker 4-3.

[0043] Among them, Figure 1 As shown, each energy storage device 4 is configured at the low voltage AC side of the box transformer 3 of the wind turbine group at the end of the corresponding collector line. The wind turbine group at the end of the collector line is the wind turbine group closest to the connection point between the collector line and the AC busbar.

[0044] The battery system 4-1 in each energy storage device 4 is connected to the first end of the inverter 4-2, the second end of the inverter 4-2 is connected to the first end of the circuit breaker 4-3, and the second end of the circuit breaker 4-3 is connected to the low-voltage AC side of the box transformer at the end of the corresponding collector.

[0045] The energy storage device 4 of the present application is used to perform charging and discharging operations when the actual power generated by the wind farm exceeds the predicted power by a deviation value greater than a preset deviation limit, so as to adjust the actual power generated to within the allowable predicted deviation range.

[0046] Therefore, the present application configures a corresponding energy storage device 4 for each collector line in the wind farm, thereby realizing a distributed energy storage system configuration.

[0047] In one embodiment of the present application, multiple wind turbines are configured on each collecting line, and each wind turbine includes: a power generation system, a converter and a box-type transformer; each collecting line is connected to the AC bus through a corresponding circuit breaker, and the AC bus is connected to a booster station, which is used to boost the AC bus voltage and then connect it to the power grid.

[0048] Specifically, in this embodiment, if Figure 1 As shown in the figure, the wind farm has a total of n collectors, each of which is equipped with m wind turbines, and an electrochemical energy storage device is incorporated into the low-voltage AC side of the wind turbine box transformer at the end of each collector. The wind turbine is mainly composed of 1-power generation system (including impeller, rotating chain and generator), 2-converter and 3-box transformer.

[0049] Each collector is connected to the AC busbar through a 6-collector circuit breaker, such as the 66kV AC busbar used in this example, and then the busbar voltage is raised to a preset volt value through an 8-boosting station and then connected to the grid, for example Figure 1 As shown, in this example, a 220kV offshore substation is used to increase the 66kV AC bus to 220kV through a step-up transformer.

[0050] It should be noted that the number of collector lines, the number of wind turbines arranged on each collector line, and the parameters of each device in the embodiment of the present application can be determined according to actual conditions, and the present application does not impose any restrictions on this. And, for the sake of convenience, Figure 1 The energy storage device 4 of only one collecting line is described as an example. The configuration and control method of the energy storage device corresponding to each collecting line are the same, and this application will not elaborate on this.

[0051] In summary, the wind farm energy storage system for reducing the wind power prediction deviation of the embodiment of the present application is configured with an electrochemical energy storage device on the low-voltage AC side of the box transformer of the wind turbine at the end of each collector line in the wind farm, thereby realizing the distributed energy storage configuration of the wind farm. Furthermore, when the deviation value between the predicted wind power and the actual result exceeds the limit, the corresponding charging and discharging operation can be performed through the energy storage device, so as to adjust the deviation between the actual power generated by the wind farm and the predicted power within the allowable range. In addition, the system can also reduce the number of distributed energy storage devices configured in the wind farm without changing the existing wind farm networking form and wind turbine structure, thereby reducing the possible risks of the distributed energy storage system, reducing the cost of the energy storage system, and improving the stability and reliability of the wind farm operation.

[0052] Based on the above embodiments, in order to more clearly illustrate the specific implementation process of reducing the wind power prediction deviation of the above wind farm energy storage system for reducing the wind power prediction deviation, a control method for reducing the wind power prediction deviation proposed in the embodiment of the present application is described in detail below. This method is applied to the wind farm energy storage system for reducing the wind power prediction deviation in the above embodiment, that is, the relevant equipment of the wind farm in the above embodiment is controlled to implement the control method of this embodiment. The executor of the control method of this embodiment can be the control device of the above wind farm energy storage system.

[0053] Figure 2 This is a flow chart of a control method for reducing wind power prediction deviation proposed in an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0054] Step S101 : obtaining the day-ahead short-term forecast data of the wind power of the wind farm, and dividing the control period of the wind farm output according to the forecast point interval in the day-ahead short-term forecast data.

[0055] It should be noted that, in order to facilitate understanding of the control method of this application, the terms involved in this application are explained below. Among them, power forecasting refers to the estimation of the electric power output of a wind farm in a period of time in the future, which is usually divided into ultra-short-term power forecasting (for example, 0-4 hours), short-term power forecasting (for example, 0-72 hours) and medium-term power forecasting (for example, 0-10 days), and each forecasting period has a time resolution, for example, 15 minutes is used in this application.

[0056] Point of Common Coupling (PCC): For the wind turbine set configured with energy storage device in this application, it refers to the common connection point where the circuit breaker outlet of the energy storage device is connected to the low-voltage side of the box transformer.

[0057] Energy storage device state of charge (SOC): Indicates the charge and discharge status of the battery in the energy storage device, used to describe the ratio of the current battery charge to the full charge.

[0058] Specifically, the day-ahead short-term forecast data output by the existing wind power forecasting system of the wind farm is first received, and the wind farm output process is divided into a plurality of control periods according to the interval between two forecast points in the acquired day-ahead short-term forecast data.

[0059] In order to facilitate understanding of the implementation process of the control method for reducing wind power prediction deviation of the present application, a specific control method in a practical application proposed in an embodiment of the present application is described below. Figure 3 The present invention provides a flowchart of a specific control method for reducing wind power prediction deviation proposed in an embodiment of the present application.

[0060] In this embodiment, if Figure 3 As shown in the figure, the short-term forecast data received by the wind farm wind power forecasting system has a forecast point of 15 minutes, and a total of 96 forecast points throughout the day. Therefore, the wind farm output is segmented and controlled at intervals of 15 minutes, and a total of 96 control periods are divided throughout the day.

[0061] Step S102: Calculate the wind power prediction real-time deviation value corresponding to each control period according to the predicted power of the wind farm and the actual power generated by each collector line.

[0062] Specifically, the real-time deviation of the wind farm power forecast in each control period is calculated according to the acquired forecast data and the actual power generated by the wind farm obtained through actual measurement in each control period.

[0063] In one embodiment of the present application, the real-time deviation of wind farm power prediction in each control period can be calculated by the following formula:

[0064]

[0065] Among them, P f (j) is the predicted power of the wind farm in the control period of time j, which can be determined from the acquired short-term forecast data of the day before. is the actual total power generated by each wind turbine on the i-th collector during the control period at time j, n is the number of collectors, β(j) is the predicted deviation of the wind power during the control period at time j, that is, the real-time deviation value of the power prediction corresponding to the control period.

[0066] Step S103, obtaining a preset power forecast deviation limit, and when the real-time deviation value corresponding to any control period is greater than the deviation limit, calculating the forecast deviation value of the wind farm actual power exceeding the allowable forecast deviation range based on the real-time deviation value.

[0067] Specifically, the allowable deviation limit of the existing wind farm wind power forecast is obtained. In this application, the limit is set to β * , the specific value can be determined according to actual needs, for example, according to the usual wind power forecast tolerance assessment value, it is set to 85%. Then, the real-time deviation value corresponding to any control period calculated in the previous step is compared with the deviation limit value, and when |β(j)|≤β * When |β(j)|>β * When the actual power generated by the wind farm exceeds the predicted deviation, the predicted deviation value of the actual power generated by the wind farm exceeding the allowable predicted deviation range is calculated based on the real-time deviation value, so that each subsequent energy storage device can perform charging and discharging operations according to the predicted deviation value.

[0068] Combine the following Figure 4 The example shown provides a detailed explanation of the fact that the actual power generated by the wind farm exceeds the prediction deviation described in the present application, that is, the difference between the actual power generated by the wind farm and the predicted power exceeds the allowable prediction deviation range.

[0069] like Figure 4 As shown in the figure, the black curve is the actual total power of the wind turbine determined by actual measurement. There is a gray prediction box every 15 minutes, which represents the allowable prediction deviation range, which can be determined by the above limit β * When the black curve falls within the gray box, it indicates that the actual total power of the wind turbine is within the predicted deviation; when the black curve exceeds the gray box, it indicates that the actual total power of the wind turbine exceeds the predicted deviation, and the part exceeding the gray box is transferred by the energy storage device through charging and discharging operations in this application.

[0070] In one embodiment of the present application, the prediction deviation value of the actual power generated by the wind farm exceeding the allowable prediction deviation range can be calculated by the following formula:

[0071]

[0072] Among them, when β(j) is greater than β * When β(j) is less than -β * When , the energy storage device is charged and the charging power is negative.

[0073] In the present application, the predicted deviation value of the wind farm actual power calculated above exceeding the allowable predicted deviation range is also the total charging and discharging power target value of the energy storage system.

[0074] Step S104, calculating the power allocation value of each energy storage device in the wind farm based on the prediction deviation value, and controlling each energy storage device to perform corresponding charging and discharging operations according to the power allocation value and the prediction deviation value, so as to adjust the actual power to within the allowable prediction deviation range.

[0075] Specifically, the power allocation value of each energy storage device is calculated according to the predicted deviation value calculated in the previous step, and each energy storage device charges or discharges the corresponding power according to the corresponding power allocation value, wherein the predicted deviation value When the value is positive, the energy storage device discharges. When the value is negative, the energy storage device is charged. By performing corresponding charging and discharging operations on the distributed energy storage device, the actual power can be adjusted to within the allowable prediction deviation range, that is, Figure 4 The actual power curve in the image is adjusted to the gray prediction box.

[0076] In one embodiment of the present application, the power allocation value of each energy storage device in the wind farm is calculated according to the predicted deviation value, including the following steps: first, according to the charge state of each energy storage device, the charge and discharge limit coefficient of each energy storage device is calculated, wherein the charge and discharge limit coefficient includes a discharge coefficient when the predicted deviation value is a positive value, and a charge coefficient when the predicted deviation value is a negative value; then, according to the charge and discharge limit coefficient of each energy storage device, the charge and discharge participation factor of each energy storage device is calculated respectively; according to the charge and discharge participation factor of each energy storage device and the predicted deviation value, the power allocation value of each energy storage device is calculated respectively.

[0077] Specifically, in this embodiment, first, according to the state of charge SOC of the energy storage device on each collector line, B,i , determine the charge and discharge limit coefficient K of each energy storage device B,i , where the charge and discharge limit coefficient can be calculated by the following formula:

[0078]

[0079] Where i represents the energy storage device on different collectors, K B_d,i Represents the discharge coefficient of the energy storage device. The discharge coefficient used for timing; K B_c,i is the charging coefficient of the energy storage device, when When it is negative, the charging factor is used.

[0080] Then, based on the charge and discharge limit coefficient of each energy storage device on the collector, the charge and discharge participation factor PF of each energy storage device is calculated. B,i , where the calculation formula is as follows:

[0081]

[0082] in,

[0083] Then, according to the charging and discharging participation factor of each energy storage device, the charging and discharging distribution power of each energy storage device is calculated respectively, where the calculation formula is as follows:

[0084]

[0085] In order to ensure that the energy storage device can be charged and discharged according to the calculated power allocation value during the charging and discharging process, the embodiment of the present application can also calculate the charging and discharging target power and track the target power during the charging and discharging process. That is, in one embodiment of the present application, after calculating the power allocation value of each energy storage device in the wind farm according to the predicted deviation value, it also includes: determining the charging and discharging target power corresponding to each energy storage device according to the maximum charging and discharging power of the energy storage device and the power allocation value of each energy storage device.

[0086] Specifically, if Figure 3 As shown, when calculating the charging and discharging target power, the charging and discharging target power of each energy storage device is limited by the charging and discharging maximum power of the energy storage device. Among them, the charging and discharging target power corresponding to each energy storage device can be calculated by the following formula:

[0087]

[0088] Furthermore, in this embodiment, controlling each energy storage device to perform corresponding charging and discharging operations includes: controlling each energy storage device to track a corresponding target power based on a grid-based control strategy.

[0089] Specifically, in this embodiment, the energy storage device of each collector line adopts a grid-following control strategy to track the corresponding target power. The grid-following control strategy means that the energy storage device is adjusted according to the operating state of the grid to achieve effective synchronization of charging and discharging with the grid.

[0090] In order to more clearly illustrate the specific implementation process of this application using the grid-based control strategy to track the target power, the following is combined with Figure 5 and Figure 6 , a target power following method proposed in an embodiment of the present application is used as an example to illustrate.

[0091] like Figure 6 As shown, when the target power is tracked in a modular manner, it can be divided into a phase-locked loop phase angle calculation module 10, a current outer loop calculation module 11 and a current inner loop calculation module 12. Figure 5 This is a flow chart of the target power following method proposed in the embodiment of the present application. Figure 5 As shown, based on the grid-based control strategy, controlling each energy storage device to track the corresponding target power includes the following steps:

[0092] Step S501, obtaining a plurality of operating state parameters of the power grid, performing phase angle calculation of a phase-locked loop based on the plurality of operating state parameters, and obtaining a power grid phase angle.

[0093] Among them, the multiple operating status parameters include the three-phase voltage of the power grid and the three-phase current output by the energy storage device.

[0094] Specifically, this step can be done by Figure 6 The phase-locked loop phase angle calculation module 10 in the embodiment is executed. The input of the module is the three-phase voltage measured by the power grid, and the output is the power grid frequency and the power grid phase angle. The calculation principle of the power grid phase angle is as follows: Figure 7 shown.

[0095] In this embodiment, the phase-locked loop phase angle calculation is performed based on multiple operating status parameters, including: first, converting the three-phase voltage of the power grid into a d-axis power grid voltage and a q-axis power grid voltage in a dq coordinate system through an initial phase; then performing PI calculation on the q-axis power grid voltage to obtain the power grid frequency, and calculating the power grid phase angle according to the power grid frequency.

[0096] Specifically, firstly, an initial phase θ is set, and the dq coordinate transformation formula T is determined according to the initial phase θ. abc-dq , and then obtain the three-phase voltage u of the power grid oabc Substitute into the conversion formula T abc-dq , calculate the d-axis grid voltage u in the dq coordinate system od and q-axis grid voltage u oq value.

[0097] Among them, the conversion formula T abc-dq As shown below:

[0098]

[0099] Then calculate the grid voltage u by the following formula od and u oq value:

[0100]

[0101] Then, the q-axis grid voltage u oq Substitute into the PI formula to calculate the grid frequency, where the grid frequency calculation formula is as follows:

[0102]

[0103] Then integrate the grid frequency ω to calculate the grid phase angle. The calculation formula is as follows:

[0104]

[0105] Here, S is a complex variable.

[0106] It should be noted that in this embodiment, if the calculated u oq If u is not close to 0, the calculated phase angle is substituted into the formulas in this step to recalculate the grid frequency and phase angle until u oq Close to 0, then lock the grid frequency and phase angle.

[0107] Step S502, performing a current outer loop calculation based on a plurality of operating state parameters and a target power, and obtaining a current control target value of the common connection point PCC of the energy storage device in a dq coordinate system.

[0108] Specifically, this step can be done by Figure 6The current outer loop calculation module 11 in the circuit is executed, and the module includes a power calculation unit 11-1 and a PI calculation unit 11-2. The input of the module is the actual measured three-phase voltage u of the power grid. oabc , the three-phase current i output by the energy storage device abc , Active power reference value Reactive power reference value The output is the PCC point dq axis current control target value i of the energy storage device dq * .

[0109] In this embodiment, the current outer loop calculation is performed based on multiple operating status parameters and target power, including: first converting the three-phase voltage and three-phase current of the power grid into the voltage in the dq coordinate system and the current in the dq coordinate system respectively; then calculating the active power of the power grid and the reactive power of the power grid according to the voltage and current in the dq coordinate system; then taking the target power as the active reference value, and calculating the d-axis current control target value and the q-axis current control target value of the common connection point PCC of the energy storage device according to the active power of the power grid, the reactive power of the power grid, the active reference value and the reactive reference value.

[0110] Specifically, the three-phase voltage u of the power grid is first converted into oabc and three-phase current i abc Convert to u odq and i dq Then substitute the voltage in the dq coordinate system and the current in the dq coordinate system on the d-axis and q-axis into the following formula to calculate the active power P of the power grid: B,i and reactive power Q B,i :

[0111]

[0112] Then, P B,i , Q B,i , and Substitute into the PI formula to calculate the energy storage device PCC point at the dq axis current control target value i dq * , including the d-axis current control target value and the q-axis current control target value, where the calculation formula is as follows:

[0113]

[0114] Among them, K p_PQ is the proportional coefficient of power PI control, K i_PQ is the integral coefficient of power PI control.

[0115] Step S503, performing a current inner loop calculation according to a plurality of operating state parameters, a grid phase angle and a current control target value, and obtaining an inverter modulation voltage control target value of the energy storage device.

[0116] Specifically, this step can be done by Figure 6 The current inner loop calculation module 12 in the embodiment is executed, and the module includes a first PI calculation unit 12-1, a second PI calculation unit 12-2, a dq coordinate transformation unit 12-3 and a dq coordinate inverse transformation unit 12-4.

[0117] In this embodiment, the current inner loop calculation is performed according to the operating state parameters, the grid phase angle and the current control target value, including the following steps: first, the current in the dq coordinate system and the current control target value in the dq coordinate system are subjected to PI calculation to obtain the voltage value corresponding to the current loop; then, the voltage value corresponding to the current loop, the voltage in the dq coordinate system and the dq axis cross-influence term are combined to perform calculations to obtain the inverter dq axis voltage control target value; then, the grid phase angle is used to perform dq coordinate inverse conversion on the inverter dq axis voltage control target value to obtain the inverter modulation voltage control target value.

[0118] Specifically, firstly, the three-phase current measurement value output by the inverter (i.e., the three-phase current i output by the energy storage device) is abc ) is converted into dq axis current i through dq transformation dq , which is converted into dq axis current i dq The conversion method can be the same as the conversion method in the above embodiment. Then, in combination with the d-axis current control target value and the q-axis current control target value calculated in step S502, the voltage value corresponding to the current loop is calculated by the PI formula, wherein the calculation formula of the voltage value corresponding to the current loop is as follows:

[0119]

[0120] Among them, K p_c is the proportional coefficient of the current inner loop PI control, K i_c It is the integral coefficient of the current inner loop PI control.

[0121] Then, by introducing the dq axis cross-influence term, the voltage value corresponding to the current loop and the voltage in the dq coordinate system are combined to perform calculations to obtain the inverter dq axis voltage control target value, where the inverter dq axis voltage control target value calculation formula is as follows:

[0122]

[0123] Where ω is the grid frequency calculated above, L f It is the inverter filter inductor.

[0124] Finally, the calculated inverter dq axis voltage control target value u dq * Substitute into the dq coordinate inverse transformation formula T dq-abc , calculate the inverter modulation voltage control target value u abc * , among which, Figure 6 As shown, the dq coordinate inverse transformation formula T dq-abc Based on the above grid phase angle θ * Determine. Among them, the dq coordinate inverse transformation formula T dq-abc As shown below:

[0125]

[0126] Inverter modulation voltage control target value u abc * The calculation formula is as follows:

[0127]

[0128] Therefore, the embodiment of the present application realizes that the energy storage device of each collector line adopts a grid-following control strategy to track the target power, further ensuring the accuracy of wind power prediction.

[0129] In summary, the control method for reducing the wind power prediction deviation of the present application can adjust the deviation between the actual power generated by the wind farm and the predicted power to within the allowable range by performing corresponding charging and discharging operations based on the real-time deviation value of the predicted wind power when the deviation value between the predicted wind power and the actual result exceeds the limit value. This method can effectively compensate for the impact of the excessive wind power prediction deviation, ensure that the actual power generated by the wind farm is within the allowable prediction deviation range, and achieve the effect of reducing the wind power prediction deviation and improving the wind power prediction accuracy. The stability and reliability of wind farm operation are improved.

[0130] In order to implement the above-mentioned embodiment, the present invention also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the control method for reducing wind power prediction deviation described in the embodiment of the second aspect of the present application is implemented.

[0131] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, if schematic expressions of the above terms are used in multiple embodiments or examples, it does not mean that these embodiments or examples are the same. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0132] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0133] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0137] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0138] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A wind farm energy storage system for reducing wind power prediction deviation, characterized in that: include: A plurality of energy storage devices, each of which corresponds to a collector line of a wind farm, and each of which comprises: a battery system, an inverter and a circuit breaker; wherein, Each of the energy storage devices is arranged at the low voltage AC side of the box transformer of the wind turbine generator set at the end of the corresponding collector line; The battery system is connected to a first end of the inverter, the second end of the inverter is connected to a first end of the circuit breaker, and the second end of the circuit breaker is connected to a low-voltage AC side of the corresponding box-type transformer; The energy storage device is used to perform charging and discharging operations when the deviation value of the actual power generated by the wind farm exceeds the predicted power by more than a preset deviation limit, so as to adjust the actual power generated to within an allowable predicted deviation range.

2. The system according to claim 1, characterized in that A plurality of wind turbines are arranged on each of the collecting lines, and each of the wind turbines comprises: a power generation system, a converter and the box-type transformer; Each of the collecting lines is connected to an AC busbar through a corresponding circuit breaker. The AC busbar is connected to a booster station. The booster station is used to boost the voltage of the AC busbar and then connect it to the power grid.

3. A control method for reducing wind power prediction deviation, characterized in that: Applied to the wind farm energy storage system for reducing wind power prediction deviation as claimed in any one of claims 1 to 2, the control method comprises the following steps: Obtaining short-term forecast data of the wind power of the wind farm a day before, and dividing the control period of the wind farm output according to the forecast point interval in the short-term forecast data a day before; Calculating the wind power prediction real-time deviation value corresponding to each control period according to the predicted power of the wind farm and the actual power generated by each collector line; Obtaining a preset power forecast deviation limit value, and when the real-time deviation value corresponding to any control period is greater than the deviation limit value, calculating a forecast deviation value of the actual power generated by the wind farm exceeding an allowable forecast deviation range based on the real-time deviation value; The power allocation value of each energy storage device in the wind farm is calculated based on the predicted deviation value, and each energy storage device is controlled to perform corresponding charging and discharging operations according to the power allocation value and the predicted deviation value, so as to adjust the actual power to within the allowable predicted deviation range.

4. The control method according to claim 3, characterized in that: The calculating the power allocation value of each energy storage device in the wind farm according to the predicted deviation value comprises: Calculating a charge and discharge limit coefficient of each energy storage device according to the charge state of each energy storage device, wherein the charge and discharge limit coefficient includes a discharge coefficient when the predicted deviation value is a positive value, and a charge coefficient when the predicted deviation value is a negative value; Calculating the charge and discharge participation factor of each energy storage device according to the charge and discharge limit coefficient of each energy storage device; The power allocation value of each energy storage device is calculated respectively according to the charge and discharge participation factor of each energy storage device and the predicted deviation value.

5. The control method according to claim 3, characterized in that: After calculating the power allocation value of each energy storage device in the wind farm according to the predicted deviation value, the method further includes: Determine the target charging and discharging power corresponding to each of the energy storage devices according to the maximum charging and discharging power of the energy storage device and the power allocation value of each of the energy storage devices; The controlling each of the energy storage devices to perform corresponding charging and discharging operations includes: Based on a grid-based control strategy, each of the energy storage devices is controlled to track the corresponding target power.

6. The control method according to claim 5, characterized in that: The controlling each of the energy storage devices to track the corresponding target power based on the grid-based control strategy includes: Acquire multiple operating state parameters of the power grid, and perform phase angle calculation of a phase-locked loop based on the multiple operating state parameters to obtain a power grid phase angle, wherein the multiple operating state parameters include a three-phase voltage of the power grid and a three-phase current output by an energy storage device; Performing a current outer loop calculation based on the multiple operating state parameters and the target power to obtain a current control target value of the common connection point PCC of the energy storage device in the dq coordinate system; An inner current loop calculation is performed according to the multiple operating state parameters, the grid phase angle and the current control target value to obtain an inverter modulation voltage control target value of the energy storage device.

7. The control method according to claim 6, characterized in that: The calculating the phase angle of the phase-locked loop based on the multiple operating state parameters comprises: The three-phase voltage of the power grid is converted into a d-axis power grid voltage and a q-axis power grid voltage in a dq coordinate system through an initial phase; The q-axis grid voltage is subjected to PI calculation to obtain a grid frequency, and the grid phase angle is calculated according to the grid frequency.

8. The control method according to claim 6, characterized in that: The performing current outer loop calculation based on the multiple operating state parameters and the target power includes: Convert the three-phase voltage and the three-phase current of the power grid into a voltage in a dq coordinate system and a current in a dq coordinate system respectively; Calculating the active power and reactive power of the power grid according to the voltage and current in the dq coordinate system; The target power is used as the active reference value, and the d-axis current control target value and the q-axis current control target value of the common connection point PCC of the energy storage device are calculated according to the grid active power, the grid reactive power, the active reference value and the reactive reference value.

9. The control method according to claim 8, characterized in that: The current inner loop calculation is performed according to the operating state parameter, the grid phase angle and the current control target value, including: Perform PI calculation on the current in the dq coordinate system and the current control target value in the dq coordinate system to obtain a voltage value corresponding to the current loop; The voltage value corresponding to the current loop, the voltage in the dq coordinate system and the dq axis cross-influence term are combined to perform calculations to obtain a dq axis voltage control target value of the inverter; The grid phase angle is used to perform dq coordinate inverse transformation on the inverter dq axis voltage control target value to obtain the inverter modulation voltage control target value.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the control method for reducing wind power prediction deviation according to any one of claims 3 to 9 is implemented.

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