Wind power energy storage collaborative frequency modulation method and device, storage medium and electronic equipment

Abstract

The application discloses a wind power energy storage collaborative frequency modulation method and device, a storage medium and an electronic device, and relates to the technical field of information. The method comprises the following steps: performing fuzzy processing on a frequency deviation, a frequency deviation change rate and a wind speed deviation to obtain the membership degrees of the frequency deviation, the frequency deviation change rate and the wind speed deviation belonging to different fuzzy language values; based on the membership degrees, calculating the activation intensity corresponding to each frequency modulation rule in a preset fuzzy rule base; according to the activation intensity, determining the overall membership degrees of a wind turbine gain and an energy storage gain belonging to different fuzzy language values; according to the overall membership degrees, performing defuzzification processing on the wind turbine gain and the energy storage gain to obtain the output values of the wind turbine gain and the energy storage gain after defuzzification; and based on the output values of the wind turbine gain and the energy storage gain after defuzzification, performing dynamic frequency modulation on a power grid. The application can improve the frequency modulation performance of the system and realize optimal allocation of wind power resources and energy storage resources under different disturbances.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to a wind power energy storage coordinated frequency regulation method, device, storage medium and electronic equipment. Background Technology

[0002] The penetration rate of renewable energy sources, represented by wind power, in the power system continues to increase. However, the inherent intermittency and volatility of wind power output severely weakens the frequency regulation capability of the power system, posing a serious challenge to grid frequency stability. To address this challenge, combining energy storage systems with wind farms has become an important technical approach to improve grid frequency stability.

[0003] Currently, existing energy storage systems and wind farms typically use a fixed control gain when coordinating frequency regulation. However, this "one-size-fits-all" control method leads to insufficient frequency regulation resources under severe disturbances, while over-regulation occurs under slight disturbances. This limits frequency regulation performance, exacerbates equipment damage, and fails to achieve optimal allocation of wind power and energy storage resources under different disturbances. Summary of the Invention

[0004] In view of this, this application provides a wind power and energy storage coordinated frequency regulation method, device, storage medium and electronic equipment, which mainly improves the frequency regulation performance of the system and realizes the optimal allocation of wind power resources and energy storage resources under different disturbances.

[0005] According to a first aspect of this application, a wind power-storage coordinated frequency regulation method is provided, the method comprising: Obtain the current frequency deviation and rate of change of frequency deviation of the power grid, as well as the currently measured wind speed deviation; The frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation are fuzzified to obtain the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values. Based on the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values, the activation intensity of each frequency modulation rule in the preset fuzzy rule base is calculated. Based on the activation intensity corresponding to each frequency modulation rule, the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​are determined respectively. Based on the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, the wind turbine gain and the energy storage gain are defuzzified to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain. Dynamic frequency regulation of the power grid is performed based on the defuzzified output values ​​of the wind turbine gain and the energy storage gain.

[0006] According to a second aspect of this application, a wind power energy storage coordinated frequency regulation device is provided, the device comprising: The acquisition unit is used to acquire the current frequency deviation and frequency deviation change rate of the power grid, as well as the currently measured wind speed deviation; The fuzzy processing unit is used to perform fuzzification processing on the frequency deviation, the frequency deviation change rate, and the wind speed deviation to obtain the membership degrees of the frequency deviation, the frequency deviation change rate, and the wind speed deviation to different fuzzy linguistic values, respectively. The calculation unit is used to calculate the activation intensity of each frequency modulation rule in the preset fuzzy rule base based on the membership degree of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation belonging to different fuzzy linguistic values. The determining unit is used to determine the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​according to the activation intensity corresponding to each frequency modulation rule; The defuzzing unit is used to defuzzify the wind turbine gain and the energy storage gain according to the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, so as to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain. The frequency regulation unit is used to dynamically regulate the frequency of the power grid based on the defuzzified output values ​​of the wind turbine gain and the defuzzified output values ​​of the energy storage gain.

[0007] According to a third aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described wind power and energy storage coordinated frequency regulation method.

[0008] According to a fourth aspect of this application, an electronic device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described wind power and energy storage coordinated frequency regulation method.

[0009] By employing the aforementioned technical solutions, this application provides a wind power and energy storage coordinated frequency regulation method, device, storage medium, and electronic equipment. Compared with existing technologies, this method fuzzifies the current frequency deviation and rate of change of the power grid, as well as the currently measured wind speed deviation, and then defuzzifies them using a preset fuzzy rule base. This enables dynamic adjustment of wind turbine gain and energy storage gain, thereby improving the system's frequency regulation performance, avoiding increased equipment losses, and achieving optimal allocation of wind power and energy storage resources under different disturbances. Furthermore, this application deeply integrates the wind-storage combined power system with a fuzzy coordination controller to form a complete coordinated frequency regulation system. This system, through fuzzy reasoning, can intelligently realize an orderly power allocation strategy that is initially dominated by energy storage, followed by wind-storage coordination in the middle stage, and finally dominated by wind turbines in the later stage.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a wind power and energy storage coordinated frequency regulation method provided in an embodiment of this application is shown. Figure 2 A schematic diagram of the wind power and energy storage combined frequency response model provided in an embodiment of this application is shown; Figure 3 A schematic diagram illustrating the fuzzy control principle provided in an embodiment of this application is shown; Figure 4 A schematic diagram of the activation intensity calculation process provided in an embodiment of this application is shown; Figure 5 A schematic diagram of the frequency deviation of the hybrid energy storage system provided in this application embodiment is shown; Figure 6 This illustration shows a schematic diagram of the wind power output under hybrid energy storage conditions provided in an embodiment of this application. Figure 7 This illustration shows a schematic diagram of the energy storage output under hybrid energy storage conditions provided in an embodiment of this application. Figure 8 A schematic diagram of a wind power energy storage coordinated frequency regulation device provided in an embodiment of this application is shown. Detailed Implementation

[0012] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0013] The existing fixed control gain method limits the frequency regulation performance of the system and cannot achieve the optimal allocation of wind power resources and energy storage resources under different disturbances.

[0014] To address the aforementioned problems, embodiments of the present invention provide a wind power and energy storage coordinated frequency regulation method, such as... Figure 1 As shown, the method includes: Step 10: Obtain the current frequency deviation and frequency deviation change rate of the power grid, as well as the current measured wind speed deviation.

[0015] This invention is primarily applicable to scenarios involving wind power and energy storage collaboratively adjusting grid frequency. The executing entity in this invention is a device or equipment capable of performing wind power and energy storage collaborative frequency regulation, specifically, it can be located on a server side.

[0016] In order to achieve coordinated frequency regulation of wind power and energy storage, this invention establishes an overall simulation architecture for frequency regulation of wind farms and hybrid energy storage systems. This architecture is based on a wind-storage combined power system, comprehensively considering the inertia characteristics of thermal power units, the virtual inertia and primary frequency regulation control characteristics of wind power units, and the dynamic response characteristics of energy storage systems at multiple time scales. System-level coordinated control and performance verification are achieved through a unified modeling and simulation platform.

[0017] In terms of system structure, the wind-storage-thermal integrated model consists of three parts: thermal power units, wind farms, and energy storage devices. The thermal power units adopt a classic speed governor (first-order dynamic model of steam turbine) to reflect its slow response characteristics and inertia support capability. The wind farm part is based on the converter control characteristics of doubly-fed induction generators, and introduces virtual synchronous control and synthetic inertia mechanism to enable the wind turbine to provide rapid power support through rotor kinetic energy release in the early stage of frequency drop. The energy storage system is connected to the grid through bidirectional DC / DC converters and inverter interfaces to achieve millisecond-level response. Its output is driven by dual signals of frequency deviation and frequency change rate.

[0018] The wind-storage combined power system mainly consists of wind turbines, energy storage devices, and thermal power units. The wind turbines primarily consist of a wind turbine and a generator, connected to the system via turbine-side and grid-side converters. The energy storage devices are connected to the system via bidirectional DC / DC converters and inverters. The thermal power units consist of a steam turbine and a synchronous generator, and can be directly connected to the system.

[0019] As wind power penetration continues to increase, this invention, based on the traditional frequency response model, introduces the wind power penetration rate variable to build a wind-storage combined power system frequency response model based on wind power penetration rate, such as... Figure 2 As shown, in Figure 2 middle, Indicates wind power penetration rate; This indicates the ratio of energy storage to wind power capacity. and These represent the reheat time constant of the thermal power unit and the total power fraction of the high-pressure steam turbine, respectively. This represents the static droop coefficient of the synchronous machine, reflecting the speed regulation characteristics of the synchronous generator; This represents the mechanical power gain coefficient, which characterizes the system's sensitivity to changes in mechanical power input. , These represent the frequency modulation inertial constant and the equivalent damping constant of the energy storage device, respectively. and These represent the inertial constant and damping constant of a system consisting only of conventional thermal power units, respectively. and These represent the virtual inertia constant and damping constant of the wind turbine, respectively. , , These represent the changes in thermal power output, wind turbine power output, and energy storage power output, respectively.

[0020] Depend on Figure 2 It can be seen that the frequency response of the wind-storage combined power system can be expressed as: (1); in, t Represents time, and These represent the frequency deviation and the rate of change of the frequency deviation of the power grid, respectively. H The system's equivalent inertia constant; The load frequency coefficient characterizes the load's self-adjustment to frequency changes; For load power; For output from thermal power turbines; Total active power output for wind farms; The total active power output of the energy storage device; This represents the equivalent droop gain of the fan. For the combined inertia gain of the wind turbine; This reflects an increase in equivalent inertia. This reflects the enhanced equivalent damping, both of which are determined by active support (wind turbines, energy storage) and conventional units.

[0021] For the frequency response of thermal power units, the thermal power channel adopts a first-order approximation of the governor and the steam turbine, and the specific formula is as follows: (2); (3); in, The rate of change of power output by the speed controller; This refers to the frequency deviation of the power grid. This refers to the output power of the speed controller; To set the output bias; The droop coefficient is... and These are the time constants of the governor and the turbine, respectively. The rate of change of power output from the thermal power turbine; This refers to the output power of a thermal power turbine.

[0022] Regarding the frequency response of wind farms, wind farms provide two types of support—droop and combined inertia / fast frequency response (FFR)—while maintaining aerodynamic feasibility and electrical constraint compliance, as detailed below: (4); (5); (6); in, Output power based on wind speed; The benchmark output of the wind farm is determined by the current wind speed; The downward component; For the composite inertia / FFR component; For droop gain; For inertia gain; For fan droop control component; To reserve an upper limit for active power margin; The allowable ramp rate for the station / converter; This is the control component for the composite inertia of the wind turbine; Represents the cumulative start time. Represents the cumulative end time; and These are the peak FFR and the upper limit of the energy budget, respectively.

[0023] To address the frequency response of energy storage, a hybrid energy storage system combines the advantages of both power-type and energy-type energy storage, constructing a complementary hybrid configuration system. Through a fuzzy coordination mechanism in the control strategy, the power-type energy storage is responsible for initial rapid inertia support and RoCoF suppression, while the energy-type energy storage handles mid-to-late-stage energy maintenance and steady-state frequency recovery. Power allocation and collaborative optimization between the two types of energy storage are achieved through dynamic gain weighting. The specific formula is as follows: (7); in, The power change rate for power-type energy storage; The power of power-type energy storage; Power gain; For fast power stage duration; The power change rate for energy-type energy storage; The power of energy storage; and These are the gain and time constant of the energy-type branch, respectively. This represents the upper limit of energy-type power. For power branch weights, It is an adjustable parameter used to set the contribution ratio of power storage in the initial response. The larger the value, the faster the system's initial response, but the relatively weaker its sustained capability, and vice versa. This provides more flexibility in optimizing the configuration. For amplitude limiting operators; Rated limits for power-type branches; Rated limits for energy-type branches; Surplus energy for energy storage; The energy-locked-out range is 0.1C-0.9C.

[0024] This invention organically combines power-type energy storage units and energy-type energy storage units, and constructs a flexibly configurable hybrid energy storage model by introducing a weighting coefficient ω. The weighting coefficient ω provides flexibility in system configuration, allowing for adjustments based on the specific needs of grid frequency regulation, focusing on suppressing the initial rate of frequency change or maintaining steady-state accuracy in the later stages. This achieves spatiotemporal complementarity of "power and energy," providing the system with full-process frequency support capabilities.

[0025] In this embodiment of the invention, changes in the frequency response of thermal power units, wind farms, and energy storage systems lead to changes in the grid frequency. Based on the deviation of the current grid frequency from the reference frequency (e.g., 50Hz), the grid frequency deviation can be determined, and the rate of change of the grid frequency deviation can be further calculated based on formula (1). At the same time, wind speed is collected in real time using sensors. Based on the collected current wind speed, the deviation of the current wind speed from the reference wind speed can be determined, i.e., the wind speed deviation.

[0026] Step 20: Perform fuzzification processing on the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to obtain the membership degree of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values.

[0027] This invention employs a fuzzy control strategy for coordinated frequency regulation of wind power and energy storage. The control system takes frequency deviation, frequency change rate, and wind speed deviation as inputs and adjusts the virtual inertia coefficient of the wind turbine and the frequency regulation gain of the energy storage in real time to achieve adaptive power distribution between wind and energy storage. The fuzzy control layer of this invention dynamically adjusts the support strength of wind power and energy storage through a three-input, two-output structure, enabling the system to achieve orderly transitions between "initial energy storage dominance, wind turbine assistance" and "mid-term wind turbine dominance, energy storage slow release" at different disturbance stages. This ensures both rapid response in the initial stage of frequency drop and stable operation during the energy recovery phase.

[0028] To balance initial frequency drop suppression and orderly recovery under conditions of sudden load changes and wind speed fluctuations, this invention constructs a three-input, two-output fuzzy coordination controller for dynamically adjusting the equivalent droop / synthetic inertia gain of wind power and the equivalent droop gain of energy storage. This controller can read the frequency deviation in real time. Frequency deviation change rate The output wind turbine gain coefficient and energy storage gain coefficient, along with the wind speed deviation, are used to scale the support strength of wind power and energy storage, respectively. The output is low-pass smoothed and then written back to the object parameters for numerical integration. The specific fuzzy control logic is as follows: Figure 3 As shown.

[0029] Before fuzzifying the frequency deviation, frequency change rate, and wind speed deviation, the fuzzy coordination controller needs to normalize these three input data. Specifically, the frequency deviation... (Unit: Hz) By range Linear normalization to ; Rate of change of frequency deviation (Unit: Hz / s) Limiting at ; wind speed deviation By interval Linear normalization to The specific formula is: (8); in, , and These are the normalized frequency deviation, frequency change rate, and wind speed deviation, respectively. , and These are the upper and lower limits of the frequency deviation amplitude, respectively. and These represent the upper and lower limits of the wind speed deviation amplitude, respectively. The reference wind speed is used.

[0030] The fuzzy coordination controller is used to allocate the active support (sag and combined inertia) of the wind turbine and the energy storage response as needed at different disturbance stages. Each integral step of the fuzzy coordination controller outputs two dimensionless gain adjustment coefficients, which are the defuzzified output values ​​of the wind turbine gain. The output value after defuzzification of energy storage gain The output value of the fan gain after defuzzification Mainly used to scale the droop gain of fans. For the inertia gain of the wind turbine The output value of energy storage gain after defuzzification Primarily used for scaling the power gain of energy storage and energy storage gain , (9); in, and The output values ​​after defuzzification of the wind turbine gain are respectively The output value after defuzzification of energy storage gain The upper and lower limits of amplitude.

[0031] In this embodiment of the invention, when performing fuzzification processing on frequency deviation, frequency change rate, and wind speed deviation, step 20 specifically includes: fuzzifying the frequency deviation based on different frequency deviation membership functions to obtain the membership degree of the frequency deviation belonging to different fuzzy linguistic values; fuzzifying the frequency deviation change rate based on different frequency deviation change rate membership functions to obtain the membership degree of the frequency deviation change rate belonging to different fuzzy linguistic values; and fuzzifying the wind speed deviation based on different wind speed deviation membership functions to obtain the membership degree of the wind speed deviation belonging to different fuzzy linguistic values.

[0032] The fuzzy linguistic values ​​for frequency deviation, frequency deviation change rate, and wind speed deviation can be set separately according to actual business needs, and these three fuzzy linguistic values ​​can be different. Furthermore, different fuzzy linguistic values ​​for frequency deviation correspond to different frequency deviation membership functions, different fuzzy linguistic values ​​for frequency deviation change rate correspond to different frequency deviation change rate membership functions, and different fuzzy linguistic values ​​for wind speed deviation correspond to different wind speed deviation membership functions.

[0033] Specifically, frequency deviation The fuzzy linguistic value of frequency deviation ,in, A negative value of 0.5 indicates a severe frequency drop. A negative value of 0 indicates a slight drop in frequency. A value of zero indicates a normal frequency. Frequency deviation change rate. The fuzzy linguistic value of the frequency deviation change rate , where, is negative big, representing a sharp drop in frequency; is negative small, representing a slow fall in frequency; is zero, representing stable frequency; is positive small, representing a slow recovery of frequency; is positive big, representing a rapid recovery of frequency. Wind speed deviation , the fuzzy linguistic values of the wind speed deviation are , where, is negative big, representing a very low wind speed; is negative small, representing a relatively low wind speed; is zero, representing that the wind speed is at the reference value; is positive small, representing a relatively high wind speed; is positive big, representing a very high wind speed.

[0034] Different fuzzy linguistic values of frequency deviation, frequency deviation change rate and wind speed deviation correspond to different membership functions. The general form of the membership function is given below, and the specific formula is as follows: (10); (11); where, is the triangular membership function, is the trapezoidal membership function, a , b , c and d are respectively the function parameters, x represents frequency deviation, frequency deviation change rate and wind speed deviation.

[0035] The difference between the membership functions of different fuzzy linguistic values only lies in the function parameters and / or function type. The membership function parameters of frequency deviation corresponding to different fuzzy linguistic values of frequency deviation ( a , b , c , d ) or ( a , b , c ) are shown in Table 1.

[0036] Table 1 Function parameters corresponding to different fuzzy linguistic values of frequency deviation

[0037] The membership function parameters of frequency deviation change rate corresponding to different fuzzy linguistic values of frequency deviation change rate ( a , b , c , d ) or ( a ,b , c ) are shown in Table 2.

[0038] Table 2 Function parameters corresponding to different fuzzy linguistic values of the rate of change of frequency deviation

[0039] Membership function parameters of wind speed deviation corresponding to different fuzzy linguistic values of wind speed deviation ( a , b , c , d ) or ( a , b , c ) are shown in Table 3.

[0040] Table 3 Function parameters corresponding to different fuzzy linguistic values of wind speed deviation

[0041] Based on the membership functions corresponding to the different fuzzy linguistic values of the frequency deviation, the rate of change of frequency deviation and the wind speed deviation described above, the specific ranges of the frequency deviation, the rate of change of frequency deviation and the wind speed deviation can be substituted into the corresponding piecewise functions for calculation, so as to obtain the membership degrees of the frequency deviation, the rate of change of frequency deviation and the wind speed deviation respectively belonging to different fuzzy linguistic values, thereby completing the fuzzification processing of the frequency deviation, the rate of change of frequency deviation and the wind speed deviation.

[0042] Further, the output of the fuzzy coordination controller (the output value after defuzzification of the wind turbine gain and the output value after defuzzification of the energy storage gain ) is defined in , the fuzzy linguistic values are , wherein S is small, representing a reduction in frequency modulation intensity; M is medium, representing maintaining a moderate frequency modulation intensity; L is large, representing an enhancement in frequency modulation intensity. The membership functions corresponding to different fuzzy linguistic values are as follows, (12); wherein represents the membership function corresponding to the fuzzy linguistic value S , represents the membership function corresponding to the fuzzy linguistic value M , represents the membership function corresponding to the fuzzy linguistic value L .

[0043] It should be noted that the fuzzy language values ​​and their corresponding membership function parameters and function types of the fuzzy coordination controller input and output in this embodiment of the invention are not limited to the above, and can be set according to actual business needs. This embodiment of the invention does not make specific limitations in this regard.

[0044] Step 30: Based on the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy language values, calculate the activation intensity corresponding to each frequency modulation rule in the preset fuzzy rule base.

[0045] The preset fuzzy rule base contains multiple frequency modulation rules. Each frequency modulation rule records the fuzzy linguistic values ​​of energy storage gain and wind turbine gain corresponding to the fuzzy linguistic values ​​of frequency deviation, frequency change rate deviation, and wind speed deviation. The specific representation of the frequency modulation rule is as follows. (13); In formula (13), each row represents a frequency modulation rule, and each frequency modulation rule is expressed in a mapping method from three inputs to two outputs, namely (frequency deviation, frequency deviation change rate, wind speed deviation). (Wind turbine gain, energy storage gain), the core idea of ​​the above frequency regulation rule is: when the frequency drops rapidly, energy storage should be used first. L When the wind speed is high, the fan should be raised appropriately. M / L ), gradually phase out energy storage as it approaches steady state / recovery phase ( S ), retain light support for the wind turbine ( S / M Frequency regulation rules can be displayed in a list. The preset fuzzy rule base contains no fewer than 20 frequency regulation rules. Its core strategy is "more aggressive during severe declines, more restrained during recovery, priority given to wind turbines at high wind speeds, and priority given to energy storage at low wind speeds".

[0046] In this embodiment of the invention, after calculating the membership degrees of frequency deviation, frequency deviation change rate, and wind speed deviation to different fuzzy linguistic values, the activation intensity corresponding to each frequency modulation rule is calculated based on these membership degrees. For this process, as follows... Figure 4 As shown, it includes: Step 31: Based on any one of the frequency modulation rules, determine the target fuzzy language values ​​involved in the frequency deviation, the frequency deviation change rate, and the wind speed deviation, respectively.

[0047] For example, for a certain frequency modulation rule, the fuzzy linguistic value of the frequency deviation is... The fuzzy linguistic value of the frequency deviation change rate The fuzzy linguistic value of wind speed deviation The aforementioned fuzzy linguistic value in this rule is the target fuzzy linguistic value.

[0048] Step 32: Based on the membership degrees of the frequency deviation, the frequency deviation change rate, and the wind speed deviation to different fuzzy linguistic values, determine the membership degrees of the target fuzzy linguistic values ​​involved in the frequency deviation, the frequency deviation change rate, and the wind speed deviation, respectively.

[0049] For example, the normalized value of the frequency deviation is -0.4. The membership degree of (negative large) is 0.0. The membership degree of (small negative) is 0.6. The membership degree of (zero) is 0.4; the normalized value of the rate of change of frequency deviation is -0.8. The membership degree of (negative large) is 0.2. The membership degree of (small negative) is 0.8. The membership degree of (zero) is 0.0; the normalized value of the wind speed deviation is 0.67. The membership degree of (Zhengda) is 1.0. This allows us to determine the membership degree of the target fuzzy linguistic values ​​involved in frequency deviation, frequency deviation change rate, and wind speed deviation in each frequency modulation rule.

[0050] Step 33: Determine the minimum membership degree based on the membership degree of the target fuzzy linguistic values ​​involved in the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation, and determine the activation intensity corresponding to any frequency modulation rule based on the minimum membership degree.

[0051] In this embodiment of the invention, after determining the membership degree of the target fuzzy linguistic values ​​involved in the frequency deviation, frequency deviation change rate, and wind speed deviation in each frequency modulation rule, the minimum value among all membership degrees is taken as the activation strength of the rule. This activation strength represents the overall credibility of the rule, and the specific formula is as follows. (14); in, For frequency modulation rules r activation intensity, Representing frequency modulation rules r Mid-frequency deviation Belongs to the i Membership degree of a fuzzy linguistic value Representing frequency modulation rules r Mid-frequency deviation change rate Belongs to the j Membership degree of a fuzzy linguistic value Representing frequency modulation rules r Medium wind speed deviation Belongs to the k The membership degree of a fuzzy linguistic value.

[0052] Following the example in step 32, let's continue with three frequency modulation rules to illustrate how to calculate the activation intensity of each rule. In frequency modulation rule 1, the target fuzzy linguistic value of the frequency deviation is... The target fuzzy linguistic value of the frequency deviation change rate is The target fuzzy linguistic value of wind speed deviation is The fuzzy linguistic value of the wind turbine gain L The fuzzy linguistic value of energy storage gain L The activation intensity of frequency modulation rule 1 is min(0.6,0.8,1.0)=0.6; the target fuzzy linguistic value of frequency deviation in frequency modulation rule 2 is... The target fuzzy linguistic value of the frequency deviation change rate is The target fuzzy linguistic value of wind speed deviation is The fuzzy linguistic value of the wind turbine gain M The fuzzy linguistic value of energy storage gain S The activation intensity of frequency modulation rule 2 is min(0.4,0.8,1.0)=0.4; the target fuzzy linguistic value of frequency deviation in frequency modulation rule 3 is... The target fuzzy linguistic value of the frequency deviation change rate is The target fuzzy linguistic value of wind speed deviation is The fuzzy linguistic value of the wind turbine gain L The fuzzy linguistic value of energy storage gain L The activation intensity of frequency modulation rule 3 is min(0.6,0.2,1.0)=0.2.

[0053] Step 40: Based on the activation intensity corresponding to each frequency modulation rule, determine the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​respectively.

[0054] In this embodiment of the invention, when determining the overall membership degree, based on the different fuzzy linguistic values ​​corresponding to the wind turbine gain, a first target rule involving the different fuzzy linguistic values ​​is determined in each frequency regulation rule; based on the activation intensity corresponding to the first target rule, the overall membership degree of the wind turbine gain belonging to the different fuzzy linguistic values ​​is determined; based on the different fuzzy linguistic values ​​corresponding to the energy storage gain, a second target rule involving the different fuzzy linguistic values ​​is determined in each frequency regulation rule; based on the activation intensity corresponding to the second target rule, the overall membership degree of the energy storage gain belonging to the different fuzzy linguistic values ​​is determined.

[0055] Specifically, for fuzzy linguistic values ​​of wind turbine gain or energy storage gain T Collect all outputs as TThe frequency modulation rule (either the first target rule or the second target rule) takes the maximum value of their activation strength, which is equivalent to the OR operation in fuzzy logic, representing the output fuzzy linguistic value. T The overall support level. The specific formula is as follows: (15); (16); in, , For the fan gain for fuzzy linguistic values T The overall membership degree, Energy storage gain for fuzzy language values T The overall membership degree, For all output fuzzy language values T The set of frequency modulation rules.

[0056] Following the example in step 33, the wind turbine gain belongs to fuzzy linguistic values. L The overall membership degree is max(0.6,0.2)=0.6; the wind turbine gain belongs to fuzzy linguistic values. M The overall membership degree is 0.4; the wind turbine gain belongs to the fuzzy linguistic value category. S The overall membership degree is 0.0. Energy storage gain belongs to the fuzzy linguistic value category. L The overall membership degree is max(0.6,0.2)=0.6; the energy storage gain belongs to the fuzzy linguistic value. S The overall membership degree is 0.4.

[0057] Step 50: Based on the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, perform defuzzification processing on the wind turbine gain and the energy storage gain to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain.

[0058] In this embodiment of the invention, when defuzzifying the wind turbine gain, output values ​​are sampled based on the output value range of the wind turbine gain to obtain multiple wind turbine gain sampling points; using different wind turbine gain output membership functions, the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values ​​is determined; based on the overall membership degree of the wind turbine gain to different fuzzy linguistic values ​​and the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values, the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values ​​is calculated; based on the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values, the maximum activation truncation membership degree corresponding to each wind turbine gain sampling point is determined; based on each wind turbine gain sampling point and its corresponding maximum activation truncation membership degree, the defuzzified output value of the wind turbine gain is calculated.

[0059] When defuzzifying the energy storage gain, the output value is sampled based on the output value range of the energy storage gain to obtain multiple energy storage gain sampling points; using different energy storage gain output membership functions, the membership degree of each energy storage gain sampling point to different fuzzy linguistic values ​​is determined; based on the overall membership degree of the energy storage gain to different fuzzy linguistic values ​​and the membership degree of each energy storage gain sampling point to different fuzzy linguistic values, the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values ​​is calculated; based on the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values, the maximum activation truncation membership degree corresponding to each energy storage gain sampling point is determined; Based on each energy storage gain sampling point and its corresponding maximum activation truncation membership degree, the defuzzified output value of the energy storage gain is calculated.

[0060] Specifically, to avoid abrupt changes in wind turbine gain and energy storage gain at fuzzy linguistic value boundaries, this embodiment of the invention employs a discrete sampling centroid algorithm, that is, within the output value range of wind turbine gain or energy storage gain. Take N=200 equidistant sampling points, where N represents the number of sampling points for wind turbine gain or energy storage gain. Calculate the value of each wind turbine gain sampling point or energy storage gain sampling point for different fuzzy language values. Activation truncation membership ,in, This represents the membership degree of each wind turbine gain sampling point or energy storage gain sampling point to different fuzzy linguistic values. This represents the overall membership degree of the wind turbine gain or energy storage gain belonging to different fuzzy linguistic values. Then, membership degrees are truncated for each wind turbine gain sampling point or energy storage gain sampling point based on the activation of different fuzzy linguistic values. Then perform OR aggregation to obtain the maximum activation cutoff membership degree corresponding to each wind turbine gain sampling point or energy storage gain sampling point. Finally, based on the sampling points of the wind turbine gain and their corresponding maximum activation truncation membership, the defuzzified output value of the wind turbine gain is calculated. Similarly, based on the energy storage gain sampling points and their corresponding maximum activation cutoff membership degrees, the defuzzified output value of the energy storage gain is calculated. , and The range is The specific formula is as follows: (17); in, The output value after deblurring, i.e. or , For all wind turbine gain sampling points or energy storage gain sampling points, for fuzzy linguistic valuesT The central value, This represents the maximum activation cutoff membership degree corresponding to the wind turbine gain sampling point or the energy storage gain sampling point.

[0061] Taking wind turbine gain as an example, this section explains the specific calculation process of the defuzzified output value. For instance, wind turbine gain belongs to the category of fuzzy linguistic values. L The overall membership degree is 0.6, and the wind turbine gain belongs to the fuzzy linguistic value category. M The overall membership degree is 0.4, and the wind turbine gain belongs to the fuzzy linguistic value category. S The overall membership degree is 0.0, within the output value range. Take 200 sampling points, assuming the first... i Each wind turbine sampling point x i =0.6, and the membership degree of the wind turbine sampling point to different fuzzy linguistic values ​​is calculated using formula (12), that is μ S (0.6) = Trapezoidal membership function (0.6; 0.0,0.0,0.15,0.3) = 0.0; μ M (0.6) = Triangle membership function (0.6; 0.2,0.4,0.6) = 0.0; μ L (0.6) = Trapezoidal membership function (0.6; 0.5, 0.7, 1.0, 1.0) = 0.5. Then, the overall membership degree is used for truncation to obtain the activation truncation membership degree of the wind turbine gain sampling point for different fuzzy linguistic values, i.e., min( μ S (0.6), 0.0) = 0.0, min( μ M (0.6), 0.4) = min(0.0, 0.4) = 0.0, min( μ L (0.6), 0.6) = min(0.5, 0.6) = 0.5, then take the maximum value to obtain the maximum activation cutoff membership degree of the sampling point of the wind turbine, that is μ (0.6) = max(0.0, 0.0, 0.5) = 0.5. Therefore, the maximum activation cutoff membership degree of all wind turbine sampling points can be calculated using the above method. Then, based on all wind turbine sampling points and their maximum activation cutoff membership degrees, the defuzzified output value of the wind turbine gain is calculated. In the specific calculation, the numerator equals... The denominator equals Assuming there are 5 wind turbine sampling points, the calculated data is shown in the table below.

[0062] Table 4 Calculation data for fan sampling points

[0063] The final calculated output value of the wind turbine gain after defuzzification is 0.72 (normalized value), and the output value of the energy storage gain after defuzzification is 0.65 (normalized value).

[0064] Step 60: Based on the defuzzified output values ​​of the wind turbine gain and the energy storage gain, perform dynamic frequency regulation on the power grid.

[0065] In this embodiment of the invention, after calculating the defuzzified output values ​​of the wind turbine gain and the energy storage gain, the defuzzified output values ​​of the wind turbine gain and the energy storage gain are respectively subjected to back-normalization processing to obtain the actual output values ​​of the wind turbine gain and the energy storage gain; using a low-pass filter, the actual output values ​​of the wind turbine gain and the energy storage gain are respectively discretized to obtain the filtered gain values ​​of the wind turbine gain and the filtered gain values ​​of the energy storage gain.

[0066] Specifically, the defuzzified output values ​​of the wind turbine gain and the energy storage gain are mapped to the engineering range. The actual output value of the fan gain is obtained. Actual output value of energy storage gain The specific formula is as follows: (18); (19); Furthermore, to prevent oscillations caused by variations in the actual output values ​​of the wind turbine gain and the energy storage gain, a first-order low-pass filter is needed for discretization before the data is applied to the system model. The specific formula is as follows: (20); (twenty one); in, The gain value that will ultimately be used for the wind turbine gain or energy storage gain at the current moment. This represents the actual output value of the wind turbine gain or energy storage gain calculated by defuzzification at the current moment. This is the filtered gain value from the previous time step. For simulating step size, is the filter time constant.

[0067] When performing dynamic frequency regulation on the power grid, the gain value after the wind turbine gain filter is compared with the wind turbine droop gain. and fan inertia gain Multiply by the energy storage gain, and simultaneously multiply the filtered gain value by the energy storage power gain. and energy storage gain Multiply to achieve dynamic frequency regulation of the power grid.

[0068] To verify the grid frequency regulation effect of the embodiments of the present invention, the embodiments of the present invention also provide specific simulation examples. The key parameter tables of hybrid energy storage and fuzzy control are shown in Table 5 and Table 6, respectively.

[0069] Table 5 Hybrid Energy Storage

[0070] Table 6 Fuzzy Control Parameters

[0071] To demonstrate the effectiveness of the embodiments of the present invention, a design was created to showcase the performance differences between the method of the embodiments of the present invention and two conventional strategies when a "load step disturbance" is combined with hybrid energy storage.

[0072] Specifically, the load step disturbance condition is used to examine the system's inertial response and primary frequency regulation performance under pure load abrupt change conditions. During the simulation, the total active load of the system is increased in a step manner at time 15 seconds, causing a momentary power shortage in the grid. At this time, wind turbines release some kinetic energy through virtual inertia control to participate in frequency regulation, the energy storage system rapidly injects power according to the frequency change rate signal, and the thermal power units subsequently enter the secondary response phase to restore steady-state equilibrium. This condition reflects the system's dynamic support capability under typical frequency drop scenarios and is a fundamental test scenario for verifying the effectiveness of the wind-storage coordinated frequency regulation model.

[0073] Furthermore, in contrast strategy 1 (wind power without frequency regulation), the energy storage is regulated separately while the wind turbine maintains maximum power tracking operation; in contrast strategy 2 (conventional droop control), both the wind turbine and the energy storage system participate in frequency regulation with fixed gain; the strategy of this embodiment (fuzzy adaptive control) uses a fuzzy coordinated controller to dynamically adjust the wind / storage frequency regulation gain.

[0074] Under a load surge of 0.08 pu, the system frequency response of the three strategies using a hybrid energy storage configuration is shown in the attached figure. Figure 5 As shown in Table 7, the specific frequency modulation results demonstrate the optimal dynamic performance of the fuzzy adaptive control strategy proposed in this embodiment.

[0075] Table 7 Frequency regulation results for each strategy in a load step scenario

[0076] As attached Figure 5As shown in Table 7, compared to the two comparative strategies, the strategy of this embodiment improves the frequency drop depth by approximately 14% and achieves faster recovery speed and smaller steady-state error. This demonstrates that fuzzy adaptive control can more effectively utilize the rapid and continuous support capabilities of hybrid energy storage to optimize the initial inertia response and steady-state recovery process of the system under severe disturbances.

[0077] Figure 6 and Figure 7 This figure illustrates the changes in active power output of the wind farm and energy storage system under the same load step condition, employing the strategy of this invention. The figure clearly reveals the core synergistic mechanism of this invention: In the initial stage of the disturbance (t=15s), the energy storage system instantaneously provides significant power support (peak value approximately 0.04 pu), undertaking the main task of suppressing the rapid frequency drop; in the mid-term recovery stage, the wind turbine output steadily increases, jointly supporting the system frequency with the energy storage; in the later steady-state stage, the energy storage output smoothly declines, with the wind turbine and thermal power units undertaking the main power balancing task.

[0078] Figure 7 The results intuitively demonstrate that the fuzzy controller achieves the ideal power distribution of "initial energy storage dominance, mid-term wind and storage synergy, and late-term wind turbine dominance", avoiding excessive operation of a single device and significantly improving the utilization efficiency of frequency regulation resources and equipment lifespan.

[0079] The above simulation results and Figure 5-7 Together, they demonstrate that the wind-storage coordinated frequency regulation system and fuzzy adaptive control method provided in the embodiments of the present invention can effectively improve the frequency stability of the system under different energy storage configurations and disturbance scenarios, and its performance is significantly better than that of traditional control methods.

[0080] This invention provides a wind power and energy storage coordinated frequency regulation method. By fuzzifying the current frequency deviation and rate of change of the power grid, as well as the currently measured wind speed deviation, and then defuzzifying them using a preset fuzzy rule base, the method can dynamically adjust the wind turbine gain and energy storage gain. This improves the system's frequency regulation performance, avoids exacerbating equipment losses, and achieves optimal allocation of wind power and energy storage resources under different disturbances. Furthermore, this invention deeply integrates the wind-storage combined power system with a fuzzy coordination controller to form a complete coordinated frequency regulation system. This system, through fuzzy inference, can intelligently implement an orderly power allocation strategy that prioritizes energy storage in the initial stage, coordinates wind and storage in the middle stage, and prioritizes wind turbines in the later stage.

[0081] Furthermore, as Figure 1 and Figure 4 The specific implementation of the method shown in this embodiment provides a wind power energy storage coordinated frequency regulation device, such as... Figure 8As shown, the device includes: an acquisition unit 101, a fuzzy processing unit 102, a calculation unit 103, a determination unit 104, a defuzzification unit 105, and a frequency modulation unit 106.

[0082] The acquisition unit 101 can be used to acquire the current frequency deviation and frequency deviation change rate of the power grid, as well as the currently measured wind speed deviation.

[0083] The fuzzy processing unit 102 can be used to fuzzify the frequency deviation, the frequency deviation change rate, and the wind speed deviation to obtain the membership degrees of the frequency deviation, the frequency deviation change rate, and the wind speed deviation to different fuzzy linguistic values.

[0084] The calculation unit 103 can be used to calculate the activation intensity of each frequency modulation rule in the preset fuzzy rule base based on the membership degree of the frequency deviation, the frequency deviation change rate, and the wind speed deviation belonging to different fuzzy language values.

[0085] The determining unit 104 can be used to determine the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​according to the activation intensity corresponding to each frequency modulation rule.

[0086] The defuzzing unit 105 can be used to defuzzify the wind turbine gain and the energy storage gain according to the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy language values, so as to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain.

[0087] The frequency modulation unit 106 can be used to dynamically adjust the frequency of the power grid based on the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain.

[0088] In some embodiments, the fuzzy processing unit 102 may be specifically used to perform fuzzy processing on the frequency deviation based on different frequency deviation membership functions to obtain the membership degree of the frequency deviation belonging to different fuzzy linguistic values; to perform fuzzy processing on the frequency deviation change rate based on different frequency deviation change rate membership functions to obtain the membership degree of the frequency deviation change rate belonging to different fuzzy linguistic values; and to perform fuzzy processing on the wind speed deviation based on different wind speed deviation membership functions to obtain the membership degree of the wind speed deviation belonging to different fuzzy linguistic values.

[0089] In some embodiments, the calculation unit 103 may be specifically configured to: determine the target fuzzy linguistic values ​​involved in the frequency deviation, the frequency deviation change rate, and the wind speed deviation respectively, based on any one of the frequency modulation rules; determine the membership degree of the target fuzzy linguistic values ​​involved in the frequency deviation, the frequency deviation change rate, and the wind speed deviation respectively, based on the membership degree of the frequency deviation, the frequency deviation change rate, and the wind speed deviation belonging to different fuzzy linguistic values; determine the minimum membership degree based on the membership degree of the target fuzzy linguistic values ​​involved in the frequency deviation, the frequency deviation change rate, and the wind speed deviation respectively; and determine the activation intensity corresponding to any one of the frequency modulation rules based on the minimum membership degree.

[0090] In some embodiments, the determining unit 104 may be specifically configured to: determine, according to the different fuzzy linguistic values ​​corresponding to the wind turbine gain, a first target rule involving the different fuzzy linguistic values ​​in each frequency regulation rule; determine, based on the activation intensity corresponding to the first target rule, the overall membership degree of the wind turbine gain to the different fuzzy linguistic values; determine, according to the different fuzzy linguistic values ​​corresponding to the energy storage gain, a second target rule involving the different fuzzy linguistic values ​​in each frequency regulation rule; and determine, based on the activation intensity corresponding to the second target rule, the overall membership degree of the energy storage gain to the different fuzzy linguistic values.

[0091] In some embodiments, the defuzzification unit 105 may be specifically used to sample output values ​​based on the output value range of the wind turbine gain to obtain multiple wind turbine gain sampling points; determine the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values ​​using different wind turbine gain output membership functions; calculate the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values ​​based on the overall membership degree of the wind turbine gain to different fuzzy linguistic values ​​and the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values; determine the maximum activation truncation membership degree corresponding to each wind turbine gain sampling point based on the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values; and calculate the defuzzified output value of the wind turbine gain based on each wind turbine gain sampling point and its corresponding maximum activation truncation membership degree.

[0092] In some embodiments, the defuzzification unit 105 may further be specifically used to sample output values ​​based on the output value range of the energy storage gain to obtain multiple energy storage gain sampling points; determine the membership degree of each energy storage gain sampling point to different fuzzy linguistic values ​​using different energy storage gain output membership functions; calculate the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values ​​based on the overall membership degree of the energy storage gain to different fuzzy linguistic values ​​and the membership degree of each energy storage gain sampling point to different fuzzy linguistic values; determine the maximum activation truncation membership degree corresponding to each energy storage gain sampling point based on the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values; and calculate the defuzzified output value of the energy storage gain based on each energy storage gain sampling point and its corresponding maximum activation truncation membership degree.

[0093] In some embodiments, the apparatus further includes a processing unit.

[0094] The processing unit is used to perform back-normalization processing on the defuzzified output values ​​of the wind turbine gain and the energy storage gain to obtain the actual output values ​​of the wind turbine gain and the energy storage gain; and to use a low-pass filter to discretize the actual output values ​​of the wind turbine gain and the energy storage gain to obtain the filtered gain values ​​of the wind turbine gain and the filtered gain values ​​of the energy storage gain.

[0095] The frequency modulation unit 106 can be specifically used to multiply the filtered gain value of the wind turbine gain by the wind turbine droop gain and the wind turbine inertia gain, respectively, and at the same time multiply the filtered gain value of the energy storage gain by the energy storage power gain and the energy storage energy gain, respectively, so as to realize dynamic frequency modulation of the power grid.

[0096] It should be noted that other corresponding descriptions of the functional units involved in the wind power energy storage coordinated frequency regulation device provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 4 The corresponding descriptions in [the document] will not be repeated here.

[0097] Based on the above, Figure 1 and Figure 4 Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method. Figure 1 and Figure 4 The wind power and energy storage coordinated frequency regulation method is shown.

[0098] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause an electronic device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0099] Based on the above, Figure 1 and Figure 4 The method shown, and Figure 8 To achieve the above objectives, the present application also provides an electronic device, specifically a personal computer, tablet computer, server, or other network device, as shown in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figure 1 and Figure 4 The wind power and energy storage coordinated frequency regulation method is shown.

[0100] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0101] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0102] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware.

[0104] This invention, through fuzzification of the current frequency deviation and rate of change of the power grid, as well as the measured wind speed deviation, and then defuzzification using a preset fuzzy rule base, enables dynamic adjustment of wind turbine gain and energy storage gain. This improves the system's frequency regulation performance, avoids exacerbating equipment losses, and achieves optimal allocation of wind power and energy storage resources under different disturbances. Furthermore, this invention deeply integrates the wind-storage combined power system with a fuzzy coordination controller to form a complete collaborative frequency regulation system. This system, through fuzzy inference, can intelligently implement an orderly power allocation strategy that prioritizes energy storage in the initial stage, coordinates wind and storage in the middle stage, and prioritizes wind turbines in the later stage.

[0105] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0106] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A wind power and energy storage coordinated frequency regulation method, characterized in that, include: Obtain the current frequency deviation and rate of change of frequency deviation of the power grid, as well as the currently measured wind speed deviation; The frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation are fuzzified to obtain the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values. Based on the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values, the activation intensity of each frequency modulation rule in the preset fuzzy rule base is calculated. Based on the activation intensity corresponding to each frequency modulation rule, the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​are determined respectively. The different fuzzy linguistic values ​​of the wind turbine gain and the energy storage gain include reducing the frequency modulation intensity, maintaining a medium frequency modulation intensity, and increasing the frequency modulation intensity. The overall membership degree represents the overall support degree belonging to the different fuzzy linguistic values. Based on the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, the wind turbine gain and the energy storage gain are defuzzified to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain. Based on the defuzzified output values ​​of the wind turbine gain and the defuzzified output values ​​of the energy storage gain, the power grid is dynamically frequency-regulated; Specifically, based on the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values, the wind turbine gain is defuzzified to obtain the defuzzified output value of the wind turbine gain, including: Based on the output value range of the wind turbine gain, multiple wind turbine gain sampling points are obtained by sampling the output value. By using the membership functions of different wind turbine gains, the membership degree of each wind turbine gain sampling point belonging to different fuzzy linguistic values ​​is determined; Based on the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values, and the membership degree of each wind turbine gain sampling point belonging to different fuzzy linguistic values, calculate the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values; Based on the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values, determine the maximum activation truncation membership degree corresponding to each wind turbine gain sampling point; Based on each wind turbine gain sampling point and its corresponding maximum activation truncation membership degree, calculate the defuzzified output value of the wind turbine gain; Based on the overall membership degree of the energy storage gain to different fuzzy linguistic values, the energy storage gain is defuzzified to obtain the defuzzified output value of the energy storage gain, including: Based on the output value range of the energy storage gain, multiple energy storage gain sampling points are obtained by sampling the output value. By using different energy storage gain output membership functions, the membership degree of each energy storage gain sampling point belonging to different fuzzy linguistic values ​​is determined respectively; Based on the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values, and the membership degree of each energy storage gain sampling point belonging to different fuzzy linguistic values, calculate the activation cutoff membership degree of each energy storage gain sampling point for different fuzzy linguistic values; Based on the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values, determine the maximum activation truncation membership degree corresponding to each energy storage gain sampling point; Based on each energy storage gain sampling point and its corresponding maximum activation truncation membership degree, the defuzzified output value of the energy storage gain is calculated.

2. The method according to claim 1, characterized in that, The process of fuzzifying the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to obtain the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values ​​includes: Based on different frequency deviation membership functions, the frequency deviation is fuzzified to obtain the membership degree of the frequency deviation to different fuzzy language values. Based on the membership functions of different frequency deviation change rates, the frequency deviation change rate is fuzzified to obtain the membership degree of the frequency deviation change rate to different fuzzy language values. Based on different membership functions of wind speed deviation, the wind speed deviation is fuzzified to obtain the membership degree of the wind speed deviation belonging to different fuzzy linguistic values.

3. The method according to claim 1, characterized in that, The calculation of the activation intensity corresponding to each frequency modulation rule in the preset fuzzy rule base, based on the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation belonging to different fuzzy linguistic values, includes: Based on any one of the frequency modulation rules, determine the target fuzzy language values ​​related to the frequency deviation, the frequency deviation change rate, and the wind speed deviation, respectively. Based on the membership degrees of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation to different fuzzy linguistic values, the membership degrees of the target fuzzy linguistic values ​​involved in the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation are determined. Determine the minimum membership degree based on the membership degrees of the target fuzzy linguistic values ​​involved in the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation, respectively. The activation intensity corresponding to any frequency modulation rule is determined based on the minimum membership degree.

4. The method according to claim 1, characterized in that, The step of determining the overall membership degree of the wind turbine gain and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​based on the activation intensity corresponding to each frequency modulation rule includes: Based on the different fuzzy linguistic values ​​corresponding to the wind turbine gain, the first target rule involving the different fuzzy linguistic values ​​in each frequency modulation rule is determined respectively; Based on the activation intensity corresponding to the first target rule, the overall membership degree of the wind turbine gain belonging to the different fuzzy linguistic values ​​is determined respectively; Based on the different fuzzy linguistic values ​​corresponding to the energy storage gain, the second target rule involving the different fuzzy linguistic values ​​in each frequency modulation rule is determined respectively; Based on the activation intensity corresponding to the second objective rule, the overall membership degree of the energy storage gain belonging to the different fuzzy linguistic values ​​is determined respectively.

5. The method according to any one of claims 1-4, characterized in that, After performing defuzzification processing on the wind turbine gain and the energy storage gain based on the overall membership degrees of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, respectively, to obtain the defuzzified output values ​​of the wind turbine gain and the energy storage gain, the method further includes: The defuzzed output values ​​of the wind turbine gain and the energy storage gain are respectively subjected to back normalization processing to obtain the actual output values ​​of the wind turbine gain and the energy storage gain. Using a low-pass filter, the actual output values ​​of the wind turbine gain and the energy storage gain are discretized to obtain the filtered gain values ​​of the wind turbine gain and the filtered gain values ​​of the energy storage gain. The dynamic frequency regulation of the power grid based on the defuzzified output values ​​of the wind turbine gain and the energy storage gain includes: The filtered gain value of the wind turbine gain is multiplied by the wind turbine droop gain and the wind turbine inertia gain, respectively. At the same time, the filtered gain value of the energy storage gain is multiplied by the energy storage power gain and the energy storage energy gain, respectively, so as to achieve dynamic frequency regulation of the power grid.

6. A wind power energy storage coordinated frequency regulation device, characterized in that, include: The acquisition unit is used to acquire the current frequency deviation and frequency deviation change rate of the power grid, as well as the currently measured wind speed deviation; The fuzzy processing unit is used to perform fuzzification processing on the frequency deviation, the frequency deviation change rate, and the wind speed deviation to obtain the membership degrees of the frequency deviation, the frequency deviation change rate, and the wind speed deviation to different fuzzy linguistic values, respectively. The calculation unit is used to calculate the activation intensity of each frequency modulation rule in the preset fuzzy rule base based on the membership degree of the frequency deviation, the rate of change of the frequency deviation, and the wind speed deviation belonging to different fuzzy linguistic values. The determining unit is used to determine the overall membership degree of the wind turbine gain belonging to different fuzzy linguistic values ​​and the overall membership degree of the energy storage gain belonging to different fuzzy linguistic values ​​according to the activation intensity corresponding to each frequency modulation rule. The different fuzzy linguistic values ​​of the wind turbine gain and the energy storage gain include reducing the frequency modulation intensity, maintaining a medium frequency modulation intensity, and increasing the frequency modulation intensity. The overall membership degree represents the overall support degree belonging to the different fuzzy linguistic values. The defuzzing unit is used to defuzzify the wind turbine gain and the energy storage gain according to the overall membership degree of the wind turbine gain and the energy storage gain belonging to different fuzzy linguistic values, so as to obtain the defuzzified output value of the wind turbine gain and the defuzzified output value of the energy storage gain. The frequency modulation unit is used to dynamically adjust the frequency of the power grid based on the defuzzified output values ​​of the wind turbine gain and the defuzzified output values ​​of the energy storage gain. The defuzzing unit is specifically used to sample output values ​​based on the output value range of the wind turbine gain, obtaining multiple wind turbine gain sampling points; using different wind turbine gain output membership functions, determine the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values; based on the overall membership degree of the wind turbine gain to different fuzzy linguistic values ​​and the membership degree of each wind turbine gain sampling point to different fuzzy linguistic values, calculate the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values; based on the activation truncation membership degree of each wind turbine gain sampling point for different fuzzy linguistic values, determine the maximum activation truncation membership degree corresponding to each wind turbine gain sampling point; and based on each wind turbine gain sampling point and its corresponding maximum activation truncation membership degree, calculate the defuzzified output value of the wind turbine gain. The defuzzing unit is further specifically used to sample output values ​​based on the output value range of the energy storage gain to obtain multiple energy storage gain sampling points; using different energy storage gain output membership functions, determine the membership degree of each energy storage gain sampling point to different fuzzy linguistic values; calculate the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values ​​based on the overall membership degree of the energy storage gain to different fuzzy linguistic values ​​and the membership degree of each energy storage gain sampling point to different fuzzy linguistic values; determine the maximum activation truncation membership degree corresponding to each energy storage gain sampling point based on the activation truncation membership degree of each energy storage gain sampling point for different fuzzy linguistic values; and calculate the defuzzified output value of the energy storage gain based on each energy storage gain sampling point and its corresponding maximum activation truncation membership degree.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

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