A wind farm spanning active power support trajectory optimization method

Abstract

The application discloses a wind farm spanning active power support trajectory optimization method, and belongs to the technical field of power grid frequency modulation; the optimization method comprises the following steps: S1, detecting the power grid frequency, judging whether frequency disturbance occurs, if yes, entering S2, otherwise, continuously detecting the power grid frequency; S2, according to the power grid frequency, wind speed and rotating speed information of a wind turbine generator, taking the target of reducing the maximum frequency deviation and inhibiting the wind turbine generator action amplitude frequency, rolling optimization is conducted on the active power support trajectory of the wind turbine generator in the wind farm; S3, judging whether the rotating speed of the wind turbine generator spans to the left side region of the MPPT curve, if yes, entering S4, otherwise, entering S5; S4, calculating the active instruction value changing with the rotating speed according to the rotating speed and mechanical power, so that the wind turbine generator stably operates in the left side region of the MPPT curve; S5, the active instruction value returns to the value before support; the frequency support capacity of the wind turbine generator is fully exerted, and the frequency stability of the power grid under high proportion of new energy grid connection is favorable.

Description

Technical Field

[0001] This invention belongs to the field of power grid frequency regulation technology, specifically relating to a method for optimizing the active power support trajectory of a wind farm. Background Technology

[0002] In emergency scenarios such as DC blocking or synchronous machine disconnection, the power system experiences a significant active power deficit, requiring wind farms to provide strong transient frequency support. Traditional inertial response operates based on the rate of frequency change, while primary frequency regulation compensates for frequency deviation. Although both methods offer some improvement in maximum frequency deviation, the effect is limited. Furthermore, this approach overemphasizes the analog synchronous machine and fails to leverage the active power regulation flexibility of wind turbines. Considering that the frequency regulation energy sources of wind farms are primarily wind energy and stored kinetic energy, the frequency regulation margin reserved by pre-emptive load shedding is insufficient to meet the strong support requirements. Additionally, the stored kinetic energy is limited. Therefore, it is urgent to research transient frequency support strategies that utilize limited energy to achieve optimal improvement in maximum frequency deviation. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method for optimizing the active power support trajectory of wind farms, thereby solving the problems in existing technologies.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A method for optimizing the active power support trajectory of a wind farm with a leapfrog pattern includes the following steps:

[0006] S1, detect the power grid frequency and determine whether a frequency disturbance has occurred. If so, proceed to S2; otherwise, continue detecting the power grid frequency.

[0007] S2, based on the grid frequency and wind speed and rotational speed information of the wind turbine, with the goal of reducing the maximum frequency deviation and suppressing the operating amplitude and frequency of the wind turbine, the active power support trajectory of the wind turbine in the wind farm is rolled for optimization.

[0008] S3. When the wind turbine completes the frequency transient support according to the active power support trajectory, determine whether the operating point of the wind turbine on the mechanical power-speed curve has crossed to the left side of the MPPT curve based on the wind turbine's speed. If so, proceed to S4; otherwise, proceed to S5.

[0009] S4 calculates the active power command value that varies with the rotational speed and mechanical power, so that the wind turbine can operate stably in the left region of the MPPT curve.

[0010] S5, the active power command value is restored to the active power command value before the wind turbine unit participated in grid frequency support.

[0011] Furthermore, in S1, the disturbance judgment criterion is: the grid frequency crosses the frequency regulation dead zone of the wind turbine, that is:

[0012] |ff n |≥Δf db

[0013] Where f is the frequency of the power grid, f n Δf is the rated frequency of the power grid. db This is the frequency regulation dead zone for wind turbine units.

[0014] Furthermore, the objective function for optimizing the active power support trajectory of the wind turbine is:

[0015]

[0016] Where J represents the objective function, Y(k+1)=[y(k+1),y(k+2),...,y(k+N)] p )] T ,U(k)=[u(k),u(k+1),...,u(k+N p -1)] T Y(k+1) and U(k) are obtained from a low-order frequency response model of a power system with wind power grid connection, N p It is the number of steps in the prediction time domain, Δf(k+i) is the frequency deviation at steps k+i, Δf(k+i-1) is the frequency deviation at steps k+i-1, and Q Y and R U Let λ be a symmetric weighted matrix. f and λ u These are weighting coefficients.

[0017] Furthermore, the symmetric weighting matrix is:

[0018]

[0019]

[0020] Among them, Q1 to Q Np For the corresponding weighting coefficients, R1 to R Np These are the corresponding weighting coefficients.

[0021] Furthermore, the low-order frequency response model of the power system with wind power grid connection can be expressed as a discrete state-space equation:

[0022]

[0023] Among them, matrices A, B, C, and D can be obtained from specific frequency response models, x(k)=[Δω(k),ΔX1(k),ΔP we1 (k),ΔPwe2 (k),...ΔP wenWT (k),ΔP TAFS1 (k),ΔP TAFS2 (k),...ΔP TAFSnWT (k)] T Let x(k+1) be the state variable of the system at step k, x(k+1) be the state variable at step k+1, Δω(k) be the wind turbine speed, ΔX1(k) be the state variable of the power system, and ΔP be the state variable of the power system. wei (k) represents the output active power of the wind turbine, ΔP TAFSi (k) represents the optimal support active power reference value for the wind turbine, i = 1, 2, ..., n WT n WT Let u(k) be the number of wind turbine units, and u(k) = [u1(k), u2(k), ... u nWT (k)] T u is the system's input variable. i (k) corresponds to ΔP without considering computational delay. TAFSi (k), d(k)=ΔP L Let y(k+1) be the disturbance variable of the system, and let y(k+1) = [Δω(k+1), 0, ... 0]. T These are the system's output variables.

[0024] Furthermore, the criterion for determining whether it has crossed into the left-hand region of the MPPT curve is: if ω wi,j ≤ω opti,j If ω = , then it crosses to the left side of the MPPT curve; otherwise, it does not cross to the left side of the MPPT curve. wi,j Let ω be the rotational speed of fan i in step j. opti,j This is the optimal speed for fan i.

[0025] Furthermore, in S4, the formula for calculating the active power command value is:

[0026]

[0027] Among them, P TPPT For the active power command to be calculated, ω tem ω is the rotational speed at which the wind turbine exits transient frequency support. min P is the lower limit of the fan speed. wm (ω tem The mechanical power-speed curve of the wind turbine shows a speed of ω. tem Power at time, P MPP (ω min The mechanical power-speed curve of the wind turbine shows a speed of ω. min Power at time, ω w This represents the current rotational speed of the fan.

[0028] A wind farm leapfrog active power support trajectory optimization system includes:

[0029] Detection module: Detects the power grid frequency and determines whether a frequency disturbance has occurred. If so, it proceeds to the trajectory optimization module; otherwise, it continues to detect the power grid frequency.

[0030] Trajectory optimization module: Based on the grid frequency and wind speed and rotational speed information of the wind turbine, the active power support trajectory of the wind turbine in the wind farm is optimized by rolling to reduce the maximum frequency deviation and suppress the operating amplitude and frequency of the wind turbine.

[0031] Area Judgment Module: When the wind turbine completes frequency transient support according to the active power support trajectory, the module determines whether the operating point of the wind turbine on the mechanical power-speed curve has crossed to the left side of the MPPT curve based on the wind turbine's speed. If so, it enters the instruction calculation module; otherwise, it enters the recovery module.

[0032] Command calculation module: Calculates the active power command value that varies with the rotational speed and mechanical power based on the rotational speed and mechanical power, so that the wind turbine can operate stably in the left region of the MPPT curve;

[0033] In addition, the instruction recovery module restores the active power instruction value to the active power instruction value before the wind turbine unit participated in grid frequency support.

[0034] A computer storage medium storing a readable program that, when the program is run, can execute the aforementioned optimization method.

[0035] An electronic device includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0036] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described optimization method.

[0037] The beneficial effects of this invention are:

[0038] 1. This invention designs a transient power point tracking curve on the left side of the MPPT curve, enabling the wind turbine to operate stably on the left side of the MPPT curve, expanding the stable operating range of the wind turbine, and improving the wind turbine's transient frequency support capability.

[0039] 2. This invention proposes a frequency active support strategy. Based on the real-time operating status of the wind farm and the frequency characteristics of the system, the active support path of each unit in the wind farm is dynamically optimized with the goal of minimizing the frequency drop depth and the minimum wind turbine amplitude frequency. This allows the wind farm to simultaneously consider its own support capacity and the system frequency response requirements, and achieves the optimal lifting effect of making reasonable use of limited support energy to reach the lowest frequency point. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is the transient power point tracking curve of the wind turbine proposed in this invention;

[0042] Figure 2 This invention provides a low-order frequency response model for a power system with wind power grid connection.

[0043] Figure 3 This is the four-machine, two-area system used in this invention;

[0044] Figure 4 This is a system frequency curve diagram of the present invention;

[0045] Figure 5 This is a curve of the active power support trajectory of a wind farm, which is the actual embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] A method for optimizing the active power support trajectory of a wind farm with a leapfrog pattern includes the following steps:

[0048] S1, detect the power grid frequency and determine whether a frequency disturbance has occurred. If so, proceed to S2; otherwise, continue detecting the power grid frequency.

[0049] When judging grid frequency disturbances: whether the grid frequency crosses the frequency regulation dead zone of the wind turbine, i.e.:

[0050] |ff n |≥Δf db

[0051] Where f is the frequency of the power grid, f n Δf is the rated frequency of the power grid. db This is the frequency regulation dead zone for wind turbine units.

[0052] S2, based on the grid frequency and wind speed and rotational speed information of the wind turbine, with the goal of reducing the maximum frequency deviation and suppressing the operating amplitude and frequency of the wind turbine, the active power support trajectory of the wind turbine in the wind farm is rolled for optimization.

[0053] The objective function to be optimized is:

[0054]

[0055] Where J represents the objective function, Y(k+1)=[y(k+1),y(k+2),...,y(k+N)] p )] T ,U(k)=[u(k),u(k+1),...,u(k+N p -1)] T The specific forms of Y(k+1) and U(k) can be obtained from the discrete state-space equations of the low-order frequency response model of a power system with wind power grid connection, N. p It is the number of steps in the prediction time domain, Δf(k+i) is the frequency deviation at steps k+i, Δf(k+i-1) is the frequency deviation at steps k+i-1, and Q Y and R U Let λ be a symmetric weighted matrix. f and λ u These are weighting coefficients.

[0056] The symmetric weighted matrix is ​​represented as:

[0057]

[0058]

[0059] Among them, Q1 to Q Np For the corresponding weighting coefficients, R1 to R Np These are the corresponding weighting coefficients.

[0060] For power systems that include wind power grid connection, this embodiment adopts the following... Figure 2 The low-order frequency response model shown incorporates a Model Predictive Control (MPC) controller required for active power support trajectory optimization. For MPC, the first step is to establish a predictive model and write its mathematical form, followed by rolling optimization based on this predictive model and the objective function. This predictive model can be used to... Figure 2 The low-order frequency response model of the power system with wind power grid connection shown is obtained by: [The specific method is as follows:] Figure 2 The low-order frequency response model is expressed as the following discrete state-space equation: Figure 2As shown, the low-order frequency response model of a power system with wind power grid connection can be expressed as the following discrete state-space equation;

[0061]

[0062] Among them, matrices A, B, C, and D can be obtained from specific frequency response models, x(k)=[Δω(k),ΔX1(k),ΔP we1 (k),ΔP we2 (k),...ΔP wenWT (k),ΔP TAFS1 (k),ΔP TAFS2 (k),...ΔP TAFSnWT (k)] T Let x(k+1) be the state variable of the system at step k, x(k+1) be the state variable at step k+1, Δω(k) be the wind turbine speed, and ΔX1(k) be the state variable of the power system, including the frequency change and the active power change of the load. These are difficult to measure directly in a wind farm and are estimated by a state observer. ΔP wei (k) represents the output active power of the wind turbine, ΔP TAFSi (k) represents the optimal support active power reference value for the wind turbine, i = 1, 2, ..., n WT n WT Let u(k) be the number of wind turbine units, and u(k) = [u1(k), u2(k), ... u nWT (k)] T u is the system's input variable. i (k) corresponds to ΔP without considering computational delay. TAFSi (k), d(k)=ΔP L Let y(k+1) be the disturbance variable of the system, and let y(k+1) = [Δω(k+1), 0, ... 0]. T These are the system's output variables.

[0063] S3. When the wind turbine completes the frequency transient support according to the active power support trajectory of S2, determine whether the operating point of the wind turbine on the mechanical power-speed curve has crossed to the left area of ​​the maximum power point tracking (MPPT) curve based on the wind turbine's speed. If so, proceed to S4; otherwise, proceed to S5.

[0064] like Figure 1As shown, when the operating point of a wind turbine on the mechanical power-speed curve crosses from the right side of the MPPT curve to the left side, it cannot operate stably on the left side due to the need for balance between electromagnetic torque and mechanical torque, and will return to its original operating point along the MPPT curve. If the active power command value of the wind turbine is continuously changed on the left side of the MPPT curve, thereby correspondingly changing its electromagnetic torque to offset the change in mechanical torque, the wind turbine can operate stably on the left side, i.e., on the Temporary Power Point Tracking (TPPT) curve. The criterion for determining whether it has crossed to the left side of the MPPT curve is...

[0065] ω wi,j ≤ω opti,j

[0066] Where, ω wi,j Let ω be the rotational speed of fan i in step j. opti,j The optimal speed of fan i is the speed corresponding to the maximum power point. If the above formula is satisfied, it crosses to the left side of the MPPT curve; otherwise, it does not cross to the left side of the MPPT curve.

[0067] S4 calculates the active power command value that varies with the rotational speed and mechanical power, so that the wind turbine can operate stably in the left region of the MPPT curve.

[0068] When the wind turbine exits transient frequency support and operates in the short-term support region, i.e., on the left side of the MPPT curve, the control process described in S4 is triggered; the active power command value varying with the rotational speed is calculated based on the rotational speed and mechanical power, so that the rotational speed stabilizes in the left region. The formula for calculating the active power command value is:

[0069]

[0070] Among them, P TPPT For the active power command to be calculated, ω tem ω is the rotational speed at which the wind turbine exits transient frequency support. min P is the lower limit of the fan speed. wm (ω tem The mechanical power-speed curve of the wind turbine shows a speed of ω. tem Power at time, P MPP (ω min The mechanical power-speed curve of the wind turbine shows a speed of ω. min Power at time, ω w The current rotational speed of the fan; P TPPT As an active command issued to the wind turbine, the wind turbine can operate stably in the left region of the MPPT curve.

[0071] S5, the active power command value is restored to the value before support, that is, the active power command value before the wind turbine participated in grid frequency support;

[0072] This embodiment is based on Figure 3 The power system architecture with a wind farm, consisting of four generators and two zones, is shown in the diagram. A simulation model was built in MATLAB / Simulink. Figure 4 As shown, a disturbance with a power loss of 0.06 pu is set at t = 10s, and the method proposed in this invention is compared with the comprehensive inertia. Figure 4 It can be seen that the maximum frequency deviation of the method proposed in this invention is smaller than the comprehensive inertia. Furthermore, wind farms using comprehensive inertia experience a secondary frequency drop after the wind turbine exits transient frequency support. The method proposed in this invention, however, allows the wind turbine to operate stably on the right side of the MPPT curve, avoiding this secondary frequency drop. Figure 5 It can be seen that the active power command changes of the method proposed in this invention are more stable.

[0073] In summary, this invention proposes a wind farm active power support trajectory optimization method that addresses the strong transient frequency support requirements under severe frequency drops in scenarios with large active power disturbances. To fully utilize the frequency support capabilities of wind turbines, a transient power point tracking method is designed in the wind turbine controller, extending the stable operating domain of the wind turbines and enabling them to release more kinetic energy without causing secondary frequency drops. Furthermore, a model predictive control-based frequency support strategy is designed in the wind farm controller, aiming to minimize the frequency drop depth and the amplitude-frequency response of the wind turbines. This strategy performs rolling optimization of the active power support path for each turbine within the wind farm, thereby reducing the maximum frequency deviation of the system.

[0074] Furthermore, those skilled in the art will clearly understand that the above methods can be implemented using software plus necessary general-purpose hardware platforms, and of course, hardware as well, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the above-described method of the present invention.

[0075] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0076] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for optimizing the active power support trajectory of a wind farm in a leapfrog manner, characterized in that, Includes the following steps: S1, detect the power grid frequency and determine whether a frequency disturbance has occurred. If so, proceed to S2; otherwise, continue detecting the power grid frequency. S2, based on the grid frequency and wind speed and rotational speed information of the wind turbine, with the goal of reducing the maximum frequency deviation and suppressing the operating amplitude and frequency of the wind turbine, the active power support trajectory of the wind turbine in the wind farm is rolled for optimization. S3. When the wind turbine completes the frequency transient support according to the active power support trajectory, determine whether the operating point of the wind turbine on the mechanical power-speed curve has crossed to the left side of the MPPT curve based on the wind turbine's speed. If so, proceed to S4; otherwise, proceed to S5. S4 calculates the active power command value that varies with the rotational speed and mechanical power, so that the wind turbine can operate stably in the left region of the MPPT curve. S5, the active power command value is restored to the active power command value before the wind turbine unit participates in grid frequency support; The objective function for optimizing the active power support trajectory of the wind turbine is: in, J This represents the objective function to be optimized. Y ( k +1) = [ y ( k +1), y ( k +2), ..., y ( k + N p )] T , U ( k ) = [ u ( k ), u ( k +1), ..., u ( k + N p -1)] T , Y ( k +1) and U ( k The value is obtained from a low-order frequency response model of a power system with wind power grid connection. N p It is the step size in the prediction time domain, Δ f ( k + i )for k + i Frequency deviation of the step, Δ f ( k + i -1) is k + i -1 step frequency deviation Q Y and R U It is a symmetric weighted matrix. λ f and λ u These are weighting coefficients; The criterion for determining whether the curve has crossed to the left side of the MPPT curve is: if If it crosses to the left side of the MPPT curve, then it crosses to the left region of the MPPT curve; otherwise, it does not cross to the left region of the MPPT curve. ω wi,j For the first j Stepper i rotational speed, ω opti,j For wind turbine i The optimal speed; In S4, the formula for calculating the active power command value is: in, P TPPT For the calculation of active power instructions, ω tem The rotational speed at which the wind turbine exits transient frequency support. ω min This is the lower limit of the fan's speed. P wm ( ω tem The speed on the wind turbine's mechanical power-speed curve is... ω tem Power at that time P MPP ( ω min The speed on the wind turbine's mechanical power-speed curve is... ω min Power at that time ω w This represents the current rotational speed of the fan.

2. The method for optimizing the active power support trajectory of a wind farm in a straddling manner according to claim 1, characterized in that, In S1, the disturbance judgment criterion is: the grid frequency crosses the frequency regulation dead zone of the wind turbine, that is: in, f For the frequency of the power grid, f n Δ is the rated frequency of the power grid. f db This is the frequency regulation dead zone for wind turbine units.

3. The method for optimizing the active power support trajectory of a wind farm in a straddling manner according to claim 1, characterized in that, The symmetric weighting matrix is: in, Q 1 to Q Np For the corresponding weighting coefficients, R 1 to R Np These are the corresponding weighting coefficients.

4. The method for optimizing the active power support trajectory of a wind farm in a straddle-type configuration according to claim 1, characterized in that, The low-order frequency response model of the power system including wind power grid connection is expressed as a discrete state-space equation: Among them, matrix A , B , C , D It is obtained from a specific frequency response model. x ( k ) = [Δ ω ( k ), Δ X 1( k ), Δ P we1 ( k ), Δ P we2 ( k ), ... Δ P wen WT( k ), Δ P TAFS1 ( k ), Δ P TAFS2 ( k ), ... Δ P TAFSn WT( k )] T For the system k The state variables of the step, x ( k +1) is k +1 step of state variables, Δ ω ( k ) represents the fan speed, Δ X 1( k ) represents the state variable of the power system, Δ P wei ( k ) represents the output active power of the wind turbine, Δ P TAFSi ( k () represents the optimal active power reference value for wind turbine support. i =1, 2, …, n WT , n WT The number of wind turbine units. u ( k ) = [ u 1( k ), u 2( k ), ... u n WT( k )] T For the system's input variables, u i ( k ) corresponding to Δ without considering the calculation delay P TAFSi ( k ), d ( k ) = Δ P L For the system's disturbance variables, y ( k +1)= [Δ ω ( k +1), 0, ... 0] T These are the system's output variables.

5. A wind farm straddle-type active power support trajectory optimization system, comprising the method described in any one of claims 1-4, characterized in that, include: Detection module: Detects the power grid frequency and determines whether a frequency disturbance has occurred. If so, it proceeds to the trajectory optimization module; otherwise, it continues to detect the power grid frequency. Trajectory optimization module: Based on the grid frequency and wind speed and rotational speed information of the wind turbine, the active power support trajectory of the wind turbine in the wind farm is optimized by rolling to reduce the maximum frequency deviation and suppress the operating amplitude and frequency of the wind turbine. Area Judgment Module: When the wind turbine completes frequency transient support according to the active power support trajectory, the module determines whether the operating point of the wind turbine on the mechanical power-speed curve has crossed to the left side of the MPPT curve based on the wind turbine's speed. If so, it enters the instruction calculation module; otherwise, it enters the recovery module. Command calculation module: Calculates the active power command value that varies with the rotational speed and mechanical power based on the rotational speed and mechanical power, so that the wind turbine can operate stably in the left region of the MPPT curve; In addition, the instruction recovery module restores the active power instruction value to the active power instruction value before the wind turbine unit participated in grid frequency support.

6. A computer storage medium storing a readable program, characterized in that, When the program runs, it can execute the optimization method described in any one of claims 1-4.

7. An electronic device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the optimization method as described in any one of claims 1-4.

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