Power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversified frequency modulation strategy

Abstract

The application relates to the technical field of power grid frequency modulation and discloses a power grid frequency dynamic analysis method containing a wind power diversification frequency modulation strategy and a dominant frequency modulation method, which is used for solving the problem of the lack of a dominant mode in the frequency response mode of the existing multiple wind power generators participating in system frequency modulation. The application establishes a time domain analysis model based on an SFR model, analyzes the section points caused by power changes and parameter changes, solves the boundary conditions of the section points, and further obtains an analytical expression; secondly, based on the analytical solution, a parameter replacement setting method between the frequency modulation modes is given respectively with the equal frequency minimum point as an index, and the frequency modulation energy contribution of each frequency modulation mode is compared, so that a theoretical basis is provided for determining the new energy dominant frequency modulation mode at the power grid level. It is concluded that, under a strong power grid environment, the inertia control and the variable coefficient droop control are both used as the dominant frequency modulation modes of the wind power generators; and under a weak power grid environment, the variable coefficient droop control is used as the dominant frequency modulation mode of the wind power generators in the weak power grid environment.

Description

Technical Field

[0001] This invention relates to the field of power grid frequency regulation technology, specifically to a power grid frequency dynamic analysis and dominant frequency regulation method incorporating diversified wind power frequency regulation strategies. Background Technology

[0002] With the continuous optimization of the energy structure, high-penetration wind power grid connection has become a key trend driving energy transformation. To improve the grid's ability to accommodate high-penetration wind power, both domestic and international grid operation guidelines stipulate that wind farms must possess the ability to rapidly adjust their active power and provide necessary inertial response and primary frequency regulation functions. Among the various frequency response modes for wind turbines participating in system frequency regulation, the most typical is integrated inertial control, which includes virtual inertial control and droop control. Its basic idea is to add an additional control loop based on a combination of frequency change rate and frequency deviation, changing the active power control reference value according to the frequency change at the wind turbine's grid connection point to compensate for disturbances in the system. However, the speed recovery process after primary frequency regulation can cause a secondary drop in system frequency, and virtual inertial control suffers from delay and large frequency change rate detection errors, leading to lower reliability. Furthermore, fixed frequency regulation parameters make tuning difficult; an excessively large gain coefficient in the frequency regulation strategy can cause wind turbines to prematurely exit frequency regulation, while an excessively small coefficient can result in unsatisfactory frequency regulation effects, failing to effectively utilize the wind farm's frequency regulation capabilities.

[0003] To address the shortcomings of traditional integrated inertia control, existing technologies propose using methods such as adjusting the droop control coefficient to improve the inertia response at the lowest frequency point, or employing a short-term power boosting response strategy. Based on variable coefficient droop control with frequency modulation segments, better frequency modulation performance can be achieved with the same frequency modulation energy as integrated inertia control by appropriately increasing the droop coefficient in the early stage of frequency modulation and decreasing it in the later stage. Short-term power boosting control allows for the setting of additional power reference values, making frequency modulation more proactive. This control injects additional constant power for a short period after a disturbance, followed by a ramp transition in the power curve, effectively reducing power deviation during frequency modulation. However, the boosted power is affected by wind speed and cannot be released based on power deficits. Unlike conventional thermal power units, the control mode of wind power participating in grid frequency regulation is determined by converter control, and multiple feasible frequency regulation modes exist. Considering the differences in economy and frequency regulation performance among different strategies, a unified mode has not yet been formed for new energy sources in the current grid, leading to the potential existence of multiple frequency regulation modes in the grid. However, the dynamic characteristics of power grid frequency with diversified frequency regulation modes of new energy sources are not clear, and there is a lack of analytical analysis and discussion on the dominant mode. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic analysis and dominant frequency regulation method for power grid frequency that incorporates diversified frequency regulation strategies for wind power, in order to solve the problem that existing frequency response modes for multiple wind turbine units participating in system frequency regulation lack a dominant mode.

[0005] The technical solution adopted by this invention to solve its technical problem is: a grid frequency dynamic analysis and dominant frequency regulation method including diversified wind power frequency regulation strategies, comprising the following steps:

[0006] S1. Constructing an SFR model under multi-mode frequency regulation of wind power.

[0007] Based on the traditional SFR model, a power control loop for wind turbine units is added to establish a dynamic SFR model with a wind power penetration rate of p.

[0008] S2. Establish a time-domain analytical model

[0009] Based on the dynamic SFR model in step S1, a time-domain analytical model of the system frequency is established, and the frequency modulation process is segmented according to the power change caused by low voltage ride-through, resulting in multiple segmentation points and time periods.

[0010] S3. Solve for the boundary conditions of each segment point.

[0011] S4. Solve for the frequency response expressions for each time period.

[0012] S5. Solve for the point of lowest frequency.

[0013] S6. Determine the dominant frequency modulation mode

[0014] The frequency regulation capabilities and adaptability of each frequency regulation mode under different scenarios were analyzed, and the frequency regulation mode with stronger adaptability and more efficient use of frequency regulation energy was selected as the dominant frequency regulation mode for wind turbine units.

[0015] Furthermore, simplifying the time-domain analytical model yields a second-order non-homogeneous differential equation. In the formula, a1, a2, a3, and a4 are coefficients; Δf(t) is the system frequency deviation; Let f(t) be the first derivative. is the second derivative of Δf(t); The disturbance power ΔP L The first derivative of (t), For ΔP e The first derivative of (t); F H The power ratio of the high-pressure cylinder of the steam turbine; T R R is the reheater time constant; M is the synchronous machine droop coefficient; D is the synchronous machine inertial time constant; ΔP is the synchronous machine damping coefficient. L (t) represents the power disturbance; ΔP e(t) represents the change in active power during the power recovery phase of the low-voltage period; k d k represents the converted virtual inertia parameter. p (t) represents the converted total droop control parameter, ΔP. e (t) represents the change in active power during the power recovery phase of the low-voltage period after conversion.

[0016] Furthermore, let the disturbance power be a step function ΔP. L (t)=u(t)ΔP L (3), where u(t) is the unit step function, ΔP L k represents the magnitude of the disturbance. d k p (t) and ΔP e The expression for (t) is:

[0017] Furthermore, for both sides of equation (1) in the interval [0, ... - ,0 + Integrating, we obtain zero initial conditions: Let be the rate of change of the frequency deviation at time 0+, and let be the value of equation (1) at the piecewise point t = t. d1 Integrating at point 1 yields the boundary conditions. Δf1(t d1- ) represents the frequency deviation at t d1- The response value at time t, Δf2(t) d1+ ) represents the frequency deviation at t d1+ The response value at time t. For frequency deviation at t d1- rate of change at time, For frequency deviation at t d1+ The rate of change at time t, for equation (1) at the piecewise point t = t d2 Integrating at point 1 yields the boundary conditions. Δf2(t d2- ) represents the frequency deviation at t d2- The response value at time t, Δf3(t) d2+ ) represents the frequency deviation at t d2+ The response value at time t. For frequency deviation at t d2- rate of change at time, For frequency deviation at t d2+ Rate of change at time; written as t = t d,3 The frequency time-domain analytical model at time t is used to define the frequency in the interval [t]. d,3- ,t d,3+ Integrating and simplifying, we get: To calculate the rate of change of the total droop control parameter after conversion, the second term in equation (8) is expanded using integration by parts and further simplified to obtain the piecewise point t. d3 Boundary conditions at time: In the formula, Δf4(t) is the frequency response expression for the fourth segment, and K p1 For wind turbine units at 0 <t<t d3 Total droop control parameter for the time period; K p2 For wind turbine units at t>t d3 The total droop control parameter for the time period, Δf3(t) d3- ) represents the frequency deviation at t d3- The response value at time t, Δf4(t) d3+ ) represents the frequency deviation at t d3+ The response value at time t. For frequency deviation at t d3- rate of change at time, For frequency deviation at t d3+ Rate of change over time.

[0018] Furthermore, in the first time period [0,t] d1 The frequency response expression is: In the formula, C 1,1 C 1,2 Let a5 be a constant coefficient and X1 be a variable. Substituting the zero initial condition (5) into equation (10) yields the constant coefficient C. 1,1 C 1,2 expression: a6 is the coefficient after combining the system parameters.

[0019] Furthermore, the second time period [t] d1 ,t d2 The frequency response expression is: k is the active power recovery rate of the wind turbine during the low-voltage ride-through recovery period, and b = -ΔP e -kt1, ΔP e Let t1 be the total change in active power caused by wind power low-voltage transmission, and t1 be the time of the first segment point. From the boundary condition (6), the constant coefficient C of the second segment can be obtained. 2,1 C 2,2 for: Furthermore, the third time period [t] d2 ,t d3 The frequency response expression is: The constant coefficient C of the third segment is obtained from the boundary condition (7). 3,1 C 3,2 :

[0020] Furthermore, the fourth time period [t] d3The frequency response expression for [T] is: The constant coefficient C of the fourth piecewise analytical expression is obtained from the boundary condition (9). 4,1 C 4,2 :

[0021] in,

[0022] Furthermore, using the maximum frequency offset and energy demand as key parameters, the substitution relationships between different modes are analyzed to explore the dominant frequency regulation mode under the same frequency regulation effect; the power recovery stage in low-voltage ride-through is simplified, and the simplified expression for the power deficit caused by wind power low-voltage ride-through is: Based on the simplified frequency response expression (18), the time of the lowest frequency is obtained. The expression for the lowest frequency point is: When a wind turbine participates in frequency regulation, the energy released is: ΔP KE This is a reference value for power change in fast power response control.

[0023] Furthermore, with the constraint that the lowest point of the frequency response before and after the substitution is equal, the frequency regulation mode substitution is achieved by setting parameters. The frequency regulation energy required by the wind turbine at the lowest point of the cutoff frequency before and after the substitution is compared to evaluate the adaptability of different frequency regulation modes in terms of energy demand, thereby determining the dominant frequency regulation mode of the wind turbine in a weak grid environment.

[0024] Furthermore, the parameter setting method for variable coefficient droop control replacing integrated inertia control: When other frequency regulation modes in the power grid remain unchanged, variable coefficient droop control is used to replace integrated inertia control in frequency regulation, satisfying: f′ min (k1)=f min (k′ d ,k′ p (22), where f min (k′ d ,k′ p f′ represents the lowest point of the system's frequency response before replacement. min (k1) represents the lowest point of the system frequency response after the replacement with variable coefficient droop control. Before the replacement, the proportion of wind turbines using integrated inertia control was x1, the proportion using variable coefficient droop control was x2, and the proportion using fast power response control was x3. After the replacement, the proportion of wind turbines using integrated inertia control is 0, the proportion using variable coefficient droop control is x1+x2, and the proportion using fast power response control is x3.

[0025] Furthermore, the parameter setting method for fast power response control replacing integrated inertia control is as follows: when other frequency regulation modes in the power grid remain unchanged, fast power response control replaces integrated inertia control in frequency regulation, satisfying: f″ min (ΔP KE )=f min (k′ d ,k′ p (23), where f″ min (ΔP KE The lowest point of the system frequency response after the replacement with fast power response control is x1. Before the replacement, the proportion of wind turbines using integrated inertia control was x2, the proportion of wind turbines using variable coefficient droop control was x3, and the proportion of wind turbines using fast power response control was x3. After the replacement, the proportion of wind turbines using integrated inertia control is 0, the proportion of wind turbines using variable coefficient droop control is x2, and the proportion of wind turbines using fast power response control is x1+x3.

[0026] The beneficial effects of the present invention are as follows: The grid frequency dynamic analysis and dominant frequency regulation method with wind power diversified frequency regulation strategy of the present invention (1) has high accuracy in frequency and differential measurement under strong grid environment, and both the integrated inertia control and variable coefficient droop control can better meet the frequency regulation requirements of the system and have good adaptability, and can be used as the dominant frequency regulation mode of wind turbine in strong grid environment. (2) Under weak grid environment, the adverse effects caused by the frequency differential link error are significantly increased, and the variable coefficient droop control has better adaptability. Moreover, in terms of maximum frequency offset and frequency regulation energy requirements, the variable coefficient droop control has the feasibility of replacing the integrated inertia control, and can be used as the dominant frequency regulation mode of wind turbine in weak grid environment. Attached Figure Description

[0027] Figure 1 This is a diagram of a traditional SFR model;

[0028] Figure 2 A diagram of an SFR model involving multiple modes in frequency modulation;

[0029] Figure 3 This is a schematic diagram of the segmentation points in the frequency modulation mode;

[0030] Figure 4 A diagram showing the setting of the sag coefficient for the first segment;

[0031] Figure 5 For the response power ΔP KE Concept art;

[0032] Figure 6 A graph showing the energy consumed up to the minimum point under different control conditions;

[0033] In the picture: Figure 2 and Figure 3The low-voltage ride-through power in this context is short for low-voltage ride-through power. Detailed Implementation

[0034] This invention first adds a power control loop for wind turbines to the traditional system frequency response model, establishing a single-unit equivalent model of the power grid with multiple modes of renewable energy participating in frequency regulation. A time-domain analytical model is constructed, and different types of segmented points are analyzed. Boundary conditions are solved in conjunction with frequency characteristics, gradually deriving the frequency response expressions for each time period. Then, based on the analytical expression of the frequency response, the minimum frequency point is obtained. Using the equality of the minimum frequency point as an indicator, parameter substitution setting methods are given for each frequency regulation mode, and the required frequency regulation energy of the wind turbine before and after substitution is compared to determine the renewable energy-dominant mode. During normal operation, the wind turbine adopts maximum power point tracking mode. When the system frequency deviates from the rated value, the wind turbine participates in the grid frequency regulation through a new power reference value. The frequency regulation modes of this invention include integrated inertia control, fast power response control, and variable coefficient droop control. The following is a detailed description of the grid frequency dynamic analysis and dominant frequency regulation method with diversified wind power frequency regulation strategies of this invention.

[0035] S1. Construct the SFR model under multi-mode frequency regulation of wind power.

[0036] like Figure 1 As shown, a power control stage for wind turbines is added to the traditional SFR model to establish a system as follows: Figure 2 The diagram shows a dynamic SFR model with a wind power penetration rate of p. The scenario is set as follows: at time 0, a grid fault occurs, causing a power disturbance to the system, and some wind power enters low-voltage ride-through. x1, x2, and x3 are the ratios of various types of wind turbines to the total number of wind turbines; s is a complex variable; F... H The power ratio of the high-pressure cylinder of the steam turbine; T R R is the reheater time constant; M is the synchronous machine droop coefficient; D is the synchronous machine inertial time constant; ΔP is the synchronous machine damping coefficient. L (t) represents the power disturbance; This represents the change in the mechanical power of the synchronizing machine; ΔP represents the total power change during frequency regulation of the wind turbine generator. e (t) represents the change in active power during the power recovery phase of the low-frequency ride-through; Δf represents the difference between the actual frequency and the rated frequency f. n The difference. Within the time scale of the wind turbine participating in frequency regulation, it is assumed that the synchronous machine start-up and shutdown plan remains unchanged, the wind speed remains unchanged, and the load is relatively fixed.

[0037] S2. Establish a time-domain analytical model

[0038] according to Figure 1 , Figure 2The frequency response model is used to establish a time-domain analytical model of the system frequency, and the result is simplified to obtain a second-order non-homogeneous differential equation. In the formula, a1, a2, a3, and a4 are coefficients; Δf(t) is the system frequency deviation; Let f(t) be the first derivative. is the second derivative of Δf(t); The disturbance power ΔP L The first derivative of (t), For ΔP e The first derivative of (t). Let the disturbance power be a step function ΔP. L (t)=u(t)ΔP L (3), where u(t) is the unit step function, ΔP L k represents the magnitude of the disturbance. d k p (t) and ΔP e The expression for (t) is: k d k represents the converted virtual inertia parameter. p (t) represents the converted total droop control parameter, ΔP. e (t) represents the change in active power during the power recovery phase of the low-voltage period after conversion.

[0039] According to the basic requirements for low-voltage ride-through in wind farms, when the grid connection voltage drops to 20% of the nominal voltage, the wind turbines in the wind farm should ensure continuous operation without disconnecting from the grid for 625ms. For wind farms that are not disconnected during a power system fault, their active power should recover to the pre-fault value at a rate of at least 20% of rated power per second from the time the fault is cleared. The segmentation time of variable coefficient droop control is 9-10 seconds. Therefore, the segmentation point caused by the change in low-voltage ride-through power occurs before the segmentation point of variable droop control. The segmentation situation is as follows: Figure 3 As shown.

[0040] S3. Solve the boundary conditions of the segmentation points.

[0041] The first type of segmentation point is the segmentation point caused by power change, where a step change in power occurs at t=0. Since there is a derivative of the step function on the right side of equation (1), i.e., the impulse function, the second derivative term on the left side of the equation... An impulse term is needed to guarantee that the equation holds true. It contains a step term at t=0. To solve for the first derivative of the frequency at t=0... + The initial value at time (0, 1) is given by equation (1) in the interval [0, 1]. - ,0 + Integrating and considering that the system frequency does not change abruptly, we obtain the zero initial condition: Let be the rate of change of the frequency deviation at time 0+. At the segmentation point, t = t d1 and t=t d2 If the power changes continuously, then the second derivative term... Containing only the step term, for equation (1) at the piecewise point t = t d1 Integrating at point 1 yields the boundary conditions. Δf1(t d1- ) represents the frequency deviation at t d1- The response value at time t, Δf2(t) d1+ ) represents the frequency deviation at t d1+ The response value at time t. For frequency deviation at t d1- rate of change at time, For frequency deviation at t d1+ The rate of change at time. For equation (1) at the piecewise point t = t d2 Integrating at point 1 yields the boundary conditions. Δf2(t d2- ) represents the frequency deviation at t d2- The response value at time t, Δf3(t) d2+ ) represents the frequency deviation at t d2+ The response value at time t. For frequency deviation at t d2- rate of change at time, For frequency deviation at t d2+ The rate of change at time t. Where Δf1(t) is the frequency response expression for the first segment, Δf2(t) is the frequency response expression for the second segment, and Δf3(t) is the frequency response expression for the third segment.

[0042] The second type of segmentation point is the segmentation point caused by changes in frequency modulation parameters, t = t d3 There is a step change in the droop coefficient, written as t = t d,3 The frequency time-domain analytical model at time t is used to define the frequency in the interval [t]. d,3- ,t d,3+ Integrating and simplifying, we get: To calculate the rate of change of the total droop control parameter after conversion, the second term in equation (8) is expanded using integration by parts, and its integral term cancels out the third term in equation (8). Further simplification yields the piecewise point t. d3 Boundary conditions at time: In the formula, Δf4(t) is the frequency response expression for the fourth segment. K p1 For wind turbine units at 0 <t<t d3 Total droop control parameter for the time period; K p2 For wind turbine units at t>td3 The total droop control parameter for the time period, Δf3(t) d3- ) represents the frequency deviation at t d3- The response value at time t, Δf4(t) d3+ ) represents the frequency deviation at t d3+ The response value at time t. For frequency deviation at t d3- rate of change at time, For frequency deviation at t d3+ Rate of change over time.

[0043] S4. Solve for the frequency response expression.

[0044] (1) First time period [0,t] d1 The frequency response expression is: In the formula, C 1,1 C 1,2 Let C be a constant coefficient, a5 be a coefficient, and X1 be a variable. Substituting the zero initial condition (5) into equation (10) yields the constant coefficient C. 1,1 C 1,2 expression: a6 is the coefficient after combining the system parameters.

[0045] (2) Second time period [t] d1 ,t d2 The frequency response expression is: k is the active power recovery rate of the wind turbine during the low-voltage ride-through recovery period, and b = -ΔP e -kt1, ΔP e Let t1 be the total change in active power caused by wind power low-voltage transmission, and t1 be the time of the first segmentation point. The constant coefficient C for the second segment can be obtained from boundary condition (6). 2,1 C 2,2 for:

[0046] (3) The third time period [t] d2 ,t d3 The frequency response expression is: The constant coefficient C of the third segment is obtained from the boundary condition (7). 3,1 C 3,2 :

[0047] (4) Fourth time period [t] d3 The frequency response expression for [T] is: The constant coefficient C of the fourth piecewise analytical expression is obtained from the boundary condition (9). 4,1 C 4,2 :

[0048] in,

[0049] Therefore, equations (10), (12), (14), and (16) constitute the mechanistic analytical expression for the frequency dynamics of the power grid under multi-mode frequency regulation with wind power. Analysis of the frequency analytical expression reveals that it involves numerous and complex parameters with intricate interactions, making it impossible to directly analyze the frequency dynamics of the power grid based solely on this expression. Based on the model of the frequency response expression, an alternative comparison method is used to evaluate the frequency regulation capability under different frequency regulation modes.

[0050] S5. Solve for the point of lowest frequency.

[0051] In actual power grid operation, frequency fluctuations are the result of the combined effects of multiple frequency regulation modes, exhibiting complex interactions and nonlinear characteristics, making it difficult to assess the adjustment effect of each frequency regulation mode on the maximum frequency offset. Therefore, using the maximum frequency offset and energy demand as key parameters, this study investigates the substitution relationships among the various modes to explore the dominant frequency regulation mode with the same frequency regulation effect.

[0052] Because the power recovery phase during wind turbine low-voltage ride-through introduces a constant term into the derivative of the expression, the timing of the minimum point cannot be directly determined by finding the derivative to zero. Therefore, the power recovery phase of low-voltage ride-through is simplified, and the simplified expression for the power deficit caused by wind power low-voltage ride-through is as follows: Based on the simplified frequency response expression (18), the time of the lowest frequency is obtained. Expression for the lowest frequency

[0053] When a wind turbine participates in frequency regulation, the energy released is: ΔP KE This is a reference value for power change in fast power response control.

[0054] S6. Determine the dominant frequency modulation mode

[0055] Depending on the grid strength of the wind farm, environments are categorized into strong grid environments and weak grid environments. Significant differences exist in frequency and differential measurement accuracy under different grid strengths, affecting the adaptability of corresponding frequency regulation modes in different scenarios. Therefore, this study analyzes the frequency regulation capabilities and adaptability of various frequency regulation modes under different scenarios, selecting the most adaptable and efficient frequency regulation mode as the dominant frequency regulation mode for wind turbines.

[0056] In a strong power grid environment, voltage fluctuations are relatively small, and frequency detection is less affected by grid strength, thus reducing the impact of frequency measurement errors and low-frequency oscillations. Considering that integrated inertia control and variable coefficient droop control can provide power support based on frequency deviations, and that integrated inertia control can provide inertia response based on the frequency change rate, it can continuously support the system's frequency regulation needs, has less impact on the transient frequency characteristics of the power system, and is more reliable and easier to implement in engineering. However, fast power response control decouples power and frequency, and the magnitude and duration of the supported power are difficult to determine. Therefore, integrated inertia control and variable coefficient droop control have higher adaptability in terms of the accuracy of power system frequency regulation needs, making them the dominant frequency regulation modes for wind turbines. In a weak power grid environment, voltage is more sensitive to reactive power, and voltage fluctuations are more pronounced after disturbances. As the strength of the wind power grid connection point decreases, the system is prone to instability, and the error of the frequency differential element is significantly affected by grid strength. The differential element in integrated inertia control has a large detection error, poor stability, and weak adaptability. In contrast, variable coefficient droop control and fast power response control do not rely on the detection of the frequency change rate, and can effectively reduce the negative impact of errors in the frequency derivative element.

[0057] By setting parameters to ensure that the lowest point of the frequency response before and after the substitution is equal, the frequency regulation mode substitution is achieved. The frequency regulation energy required by the wind turbine at the lowest point of the cutoff frequency before and after the substitution is compared to evaluate the adaptability of different frequency regulation modes in terms of energy demand, thereby determining the dominant frequency regulation mode of the wind turbine in a weak grid environment.

[0058] Given the system's conventional synchronous turbine parameters, wind power penetration level, system disturbances, and delay magnitudes, the system's lowest frequency is determined solely by the wind turbine's frequency regulation parameters. To simplify the problem, typical values ​​are assigned to variables in the expression for the system's lowest frequency that are unrelated to the wind turbine's frequency regulation parameters. Typical system parameters are used: F H =0.2, T R =20, R=0.05, M=12.6, D=1, let the system fundamental frequency f n At 50Hz, the power disturbance ΔP L = -0.1 pu, wind power accounts for 0.35. Let the total number of wind turbines be 1, the proportion of wind turbines using integrated inertia control be x1, the proportion of wind turbines using variable coefficient droop control be x2, the proportion of wind turbines using integrated inertia control be x3, and the proportion of wind turbines entering low voltage ride-through be x4.

[0059] Parameter setting method for variable coefficient droop control instead of integrated inertia control: When other frequency regulation modes in the power grid remain unchanged, variable coefficient droop control is used to replace integrated inertia control in frequency regulation, satisfying: f′ min (k1)=f min (k′d ,k′ p (22). In the formula, f min (k′ d ,k′ p f′ represents the lowest point of the system's frequency response before replacement. min (k1) represents the lowest point of the system frequency response after the replacement with variable coefficient droop control. Before the replacement, the proportion of wind turbines using integrated inertia control was x1, the proportion using variable coefficient droop control was x2, and the proportion using fast power response control was x3. After the replacement, the proportion of wind turbines using integrated inertia control is 0, the proportion using variable coefficient droop control is x1+x2, and the proportion using fast power response control is x3.

[0060] Due to the differences in the system's frequency dynamic characteristics before and after the substitution, numerous trigonometric and exponential terms appear in the substitution relationship, making it impossible to intuitively express the relationship between the first-segment droop control coefficient after substitution and the integrated inertia control parameters before substitution using traditional analytical methods. Therefore, a numerical calculation method is adopted, and the relationship between the frequency modulation parameters before and after substitution is visually illustrated through a three-dimensional coordinate graph of the numerical solution. The range of values ​​for the integrated inertia frequency modulation control parameters is set as: k d ∈[5, 15], k p ∈[10, 30]. The parameter setting relationships for the integrated inertia control section in frequency modulation, obtained by replacing the variable coefficient droop control with variable coefficient droop control, are as follows: Figure 4 As shown. Figure 4 In the diagram, the x-axis represents the virtual inertia coefficient k when wind power frequency regulation uses integrated inertial control. d The y-axis represents the droop control coefficient k when using integrated inertial control for wind power frequency regulation. p The z-axis represents the first segment coefficient k1 when wind power frequency regulation is replaced by variable coefficient droop control. The energy at the lowest point before and after the replacement is calculated as follows: Figure 6 As shown, scenario one is the frequency modulation energy required at the lowest point of the system response cutoff frequency before replacement, and scenario two is the frequency modulation energy required after replacing the integrated inertia control with variable coefficient droop control.

[0061] Parameter setting method for fast power response control replacing integrated inertia control: When other frequency regulation modes in the power grid remain unchanged, fast power response control replaces integrated inertia control in frequency regulation, satisfying: f″ min (ΔP KE )=f min (k′ d ,k′ p (23). In the formula, f″ min (ΔP KE(x1) represents the lowest point of the system frequency response after replacement with fast power response control. Before replacement, the proportion of wind turbines using integrated inertia control was x1, the proportion using variable coefficient droop control was x2, and the proportion using fast power response control was x3. After replacement, the proportion of wind turbines using integrated inertia control is 0, the proportion using variable coefficient droop control is x2, and the proportion using fast power response control is x1+x3. A three-dimensional coordinate graph is used to illustrate the substitution relationship between the frequency regulation parameters of the wind turbines before and after replacement. The value range of the frequency regulation control parameters for integrated inertia control is set to: k d ∈[5, 15], k p ∈[10, 30]. Solving for the substitution relationship between the two yields the following: Figure 5 As shown in the figure. The x-axis represents the virtual inertia coefficient k when wind power frequency regulation uses integrated inertial control. d The y-axis represents the droop control coefficient k when using integrated inertial control for wind power frequency regulation. p The z-axis represents the per-unit parameter ΔP when fast power response control is used as an alternative. KE Calculate the energy of both before and after the substitution up to the lowest point, as shown below. Figure 6 As shown in the figure, scenario one represents the frequency modulation energy required at the lowest point of the system cutoff frequency before replacement, and scenario three represents the frequency modulation energy required after replacing integrated inertia control with fast power response control.

[0062] Under the same frequency modulation effect at the lowest frequency point, the energy required after the variable coefficient droop control replaces the integrated inertia control is less than the energy required before the replacement, and at k d The larger the value, the less energy is consumed compared to integrated inertia control. It can also be seen that variable coefficient droop control is a better substitute than fast power response control. Although at k... d Larger and k p When the skewness is small, fast power response control requires less energy to replace integrated inertia control than before, but at k p and k d Over most of the variation range, fast power response control requires more energy than integrated inertia control when replacing it.

[0063] In weak grid environments, variable coefficient droop control can effectively reduce the negative impact of frequency differential errors and requires less energy for the same frequency deviation. Therefore, variable coefficient droop control is a feasible alternative to integrated inertia control. Furthermore, variable coefficient droop control exhibits greater adaptability in terms of frequency detection requirements and frequency regulation energy demands, making it a viable option as the dominant frequency regulation mode for wind turbines in weak grid environments.

[0064] In summary, (1) under strong power grid conditions, the accuracy of frequency and its differential measurement is high, and both integrated inertia control and variable coefficient droop control can well meet the frequency regulation requirements of the system and have good adaptability, making them the dominant frequency regulation mode for wind turbines under strong power grid conditions. (2) under weak power grid conditions, the adverse effects of errors in the frequency differential link increase significantly, and variable coefficient droop control has better adaptability. Moreover, in terms of maximum frequency offset and frequency regulation energy requirements, variable coefficient droop control is a feasible alternative to integrated inertia control, making it the dominant frequency regulation mode for wind turbines under weak power grid conditions.

Claims

1. A power grid frequency dynamic analysis and dominant frequency modulation method containing wind power diversification frequency modulation strategy, characterized in that, Comprising the following steps: S1, constructing SFR model under wind power multi-mode frequency modulation On the basis of the traditional SFR model, the power control link of the wind turbine is added, and the dynamic SFR model with wind power penetration rate p is established; S2, establishing time domain analytical model According to the dynamic SFR model of step S1, the time domain analytical model of system frequency is established, and the frequency modulation process is segmented according to the power change caused by low voltage ride through, to obtain multiple segment points and time periods; S3, solving the boundary conditions of each segment point S4, solving the frequency response expression of each time period S5, solving the frequency minimum point S6, determining the dominant frequency modulation mode The frequency modulation capacity and adaptability of each frequency modulation mode under different scenarios are analyzed, and the frequency modulation mode with stronger adaptability and efficient use of frequency modulation energy is selected as the dominant frequency modulation mode of the wind turbine; A second-order non-homogeneous differential equation is obtained by simplifying the time-domain analytical model wherein, a1, a2, a3, a4 are coefficients; Δf(t) is a system frequency deviation; is a first-order derivative of Δf(t), is a second-order derivative of Δf(t); is a disturbance power ΔP L (t) is a first-order derivative of ΔP (t) is a first-order derivative of ΔP e (t) is a first-order derivative of ΔP H F is a high-pressure cylinder work proportion of a steam turbine; T R R is a synchronous machine regulation coefficient; M is a synchronous machine inertia time constant; D is a synchronous machine damping coefficient; ΔP L (t) is a power disturbance; ΔP e (t) is an active power change amount in a power recovery stage during a low-pass period; k d is a converted virtual inertia parameter, k p (t) is a converted total droop control parameter, ΔP e (t) is a converted active power change amount in a power recovery stage during a low-pass period; Integrating equation (1) over the interval [0 - ,0 + ] gives the zero initial condition: Δf(0 + )=0(3), Integrating equation (1) at the segment point t=t d1 gives the boundary condition Δf1(t d1- )=Δf2(t d1+ )(4), where Δf1(t d1- ) is the response value of the frequency deviation at t d1- , Δf2(t d1+ ) is the response value of the frequency deviation at t d1+ , is the rate of change of the frequency deviation at t d1- , is the rate of change of the frequency deviation at t d1+ , and integrating equation (1) at the segment point t=t d2 gives the boundary condition Δf2(t d2- )=Δf3(t d2+ )(5), where Δf2(t d2- ) is the response value of the frequency deviation at t d2- , Δf3(t d2+ ) is the response value of the frequency deviation at t d2+ , is the rate of change of the frequency deviation at t d2- , is the rate of change of the frequency deviation at t d2+ ; the frequency time-domain analytical model at t=t d,3 is written, and integrating it over the interval [t d,3- , t d,3+ ] gives: is the rate of change of the total droop control parameter after conversion, the second term in equation (6) is expanded by the method of partial integration, and further simplification gives the boundary condition at t d3 : where Δf4(t) is the frequency response expression of the fourth segment, K p1 is the total droop control parameter of the wind turbine in the time period 0<t<t d3 ; K p2 is the total droop control parameter of the wind turbine in the time period t>t d3 , and Δf3(t d3- ) is the response value of the frequency deviation at t d3- the response value of the frequency deviation at time t d3+ , Δf4(t d3+ ) is the response value of the frequency deviation at time t , and Δf4(t d3- ) is the rate of change of the frequency deviation at time t , and Δf4(t d3+ ) is the rate of change of the frequency deviation at time t 2. The method for dynamic analysis of power grid frequency and dominant frequency regulation including diversified frequency regulation strategies of wind power according to claim 1, characterized in that, The disturbance power is a step function ΔP L (t) = u(t) ΔP L (8), where u(t) is a unit step function, ΔP L is the disturbance size, k d , k p (t) and ΔP e (t) are given by: 3.The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy of claim 1, wherein, The frequency response expression of the first time period [0, t d1 ] is In the formula, C 1,1 , C 1,2 are constant coefficients, a5 is a coefficient, X1 is a variable, and the constant coefficients C 1,1 , C 1,2 are obtained by substituting the zero initial condition (3) into the formula (10) a6 is a coefficient after combination of system parameters. 4.The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy of claim 3, wherein, The frequency response expression of the second time period [t d1 , d2 ] is: k is the active power recovery rate of the fan in the wind power low voltage ride through recovery period, b = -ΔP e -kt1, ΔP e is the total change of active power caused by wind power low penetration, t1 is the time of the first segment point, and the constant coefficient C 2,1 , 2,2 of the second segment is obtained from the boundary condition (4):

5. The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy according to claim 4, characterized in that, The frequency response expression for the third time interval [t d2 , d3 ] is: The constant coefficients C 3,1 , C 3,2 for the third segment are obtained from the boundary condition (5): 6.The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy of claim 5, wherein, The frequency response expression of the fourth time interval [t d3 The constant C 4,1 4,2 : wherein ​​ 7.The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy of claim 6, wherein, With the maximum frequency deviation and energy demand as the key parameters, the mutual replacement relationship between each mode is analyzed to explore the dominant frequency modulation mode under the same frequency modulation effect. The power recovery stage in low voltage ride through is simplified, and the power shortage expression caused by wind power low voltage ride through is: According to the simplified frequency response expression (18), the time of the lowest frequency point is obtained The expression of the lowest frequency point When the wind turbine participates in frequency modulation, the released energy is: ΔP KE The power change reference value for fast power response control. 8.The power grid frequency dynamic analysis and dominant frequency modulation method with wind power diversity frequency modulation strategy of claim 7, wherein, With the constraint that the frequency response minimum points before and after replacement are equal, the replacement of the frequency modulation mode is realized through parameter setting, and the frequency modulation energy required by the wind turbine at the minimum point of the cut-off frequency before and after replacement is compared to evaluate the adaptability of different frequency modulation modes in terms of energy demand, so as to determine the dominant frequency modulation mode of the wind turbine in weak grid environment.

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