Frequency modulation parameter optimization method and system for wind power high-permeability power system

Abstract

The invention discloses a frequency modulation parameter optimization method for a wind power high-permeability power system. The method comprises the following steps: acquiring data information of a target power system; constructing a dynamic response model and a constraint condition for providing frequency modulation support by each wind power plant under frequency disturbance; constructing a multi-unit frequency dynamic model considering spatial distribution under frequency disturbance and constraint conditions; constructing a wind power high-permeability power system frequency modulation parameter optimization model considering frequency space distribution; and simultaneous solution is carried out on the constructed model, and frequency modulation parameter optimization of the wind power high-permeability power system is completed. The invention also discloses a system for realizing the frequency modulation parameter optimization method of the wind power high-permeability power system. According to the method, optimization of the frequency modulation parameters of the wind power high-permeability power system is achieved, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, specifically relating to a method and system for optimizing frequency regulation parameters in a wind power high-penetration power system. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, the penetration rate of renewable energy sources such as wind power in the power system continues to increase, leading to a decline in the overall inertia and frequency regulation capability of the power system, and a significant increase in frequency security risks. Therefore, the power system urgently needs the active participation of new energy sources in frequency support.

[0004] Wind turbine rotor kinetic energy control relies on the converter to rapidly release rotor kinetic energy, offering advantages such as fast response and low cost. Based on the release of rotor kinetic energy at the turbine side, grid-connected wind turbines can simulate synchronous machine characteristics through the control of the grid-side converter, thereby actively participating in system frequency regulation. Currently, in existing wind farm frequency support studies, power system models mostly adopt the inertial center assumption, and evaluation and control strategies focus on individual wind farms, paying less attention to the impact of differences in the spatial distribution of frequency regulation resources in the system on the dynamic process of wind farm frequency support. Moreover, while existing schemes, represented by the inertial center frequency of the system frequency response, can characterize the overall system dynamics, they are difficult to reflect the differences in frequency spatial distribution and regional support of wind farms. Therefore, the reliability and accuracy of existing schemes are not high. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method for optimizing frequency regulation parameters in a high-penetration wind power system that is both reliable and accurate.

[0006] The second objective of this invention is to provide a system for optimizing the frequency regulation parameters of the aforementioned wind power high-penetration power system.

[0007] The frequency regulation parameter optimization method for a wind power high-penetration power system provided by this invention includes the following steps:

[0008] S1. Obtain data information about the target power system;

[0009] S2. Based on the data obtained in step S1, construct a dynamic response model and constraints for each wind farm to provide frequency regulation support under frequency disturbances;

[0010] S3. Based on the data obtained in step S1, construct a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbances;

[0011] S4. Based on the data obtained in step S1, construct an optimization model for frequency regulation parameters of a wind power system with high wind power penetration considering frequency spatial distribution;

[0012] S5. Solve the simultaneous equations of the models constructed in steps S2 to S4 to complete the optimization of frequency regulation parameters for the wind power high penetration power system.

[0013] Step S2, which involves constructing a dynamic response model for each wind farm to provide frequency regulation support under frequency disturbances, specifically includes the following steps:

[0014] Construct dynamic models of wind turbine mechanical power for each wind farm:

[0015] In the formula Let be the mechanical power of the a-th wind turbine in the i-th wind farm at time t; air density; The swept area of ​​the fan blades; Let be the wind speed of the a-th wind turbine in the i-th wind farm; Let be the wind energy capture coefficient of the a-th wind turbine in the i-th wind farm at time t, and , Let be the tip speed ratio of the a-th wind turbine in the i-th wind farm at time t. , Let be the blade radius of the a-th wind turbine in the i-th wind farm. Let be the rotational speed of the a-th wind turbine in the i-th wind farm at time t. Let be the pitch angle of the a-th wind turbine in the i-th wind farm at time t;

[0016] Construct a dynamic model differentiating the speed differences of wind turbines in various wind farms:

[0017] In the formula For discretization time interval; Let be the electromagnetic power of the a-th wind turbine in the i-th wind farm at time t; This refers to the mechanical rotational inertia of the wind turbine blades.

[0018] Construct frequency regulation power allocation models for the frequency regulation support of wind turbines in various wind farms:

[0019] In the formula Let be the frequency regulation margin weighting factor for the a-th wind turbine in the i-th wind farm; Let be the energy state index of the a-th wind turbine in the i-th wind farm at time t, and , Let be the lower limit of the rotational speed of the a-th wind turbine in the i-th wind farm. Let $\frac{a}{i}$ be the upper limit of the rotational speed of the $a$-th wind turbine in the $i$-th wind farm. Let be the number of wind turbines in the i-th wind farm; Let be the inertial time constant of the a-th wind turbine in the i-th wind farm; Let be the total inertial time constant of the i-th wind farm; Let be the total wind turbine capacity of the i-th wind farm; Let a be the rated capacity of the a-th wind turbine in the i-th wind farm; Let be the active-frequency droop coefficient of the a-th wind turbine in the i-th wind farm; is the total active power-frequency droop coefficient for the i-th wind farm; Let be the frequency regulation power undertaken by the a-th wind turbine in the i-th wind farm at time t; Let be the rate of frequency change of the i-th wind farm access node at time t; The system's rated frequency; The frequency deviation between the i-th wind farm access node and the rated frequency at time t; Let be the electromagnetic power output of the a-th wind turbine in the i-th wind farm at time t.

[0020] The constraints in step S2 specifically include the following steps:

[0021] Establish operational constraints for wind turbines in each wind farm to provide frequency regulation support under frequency disturbances:

[0022] In the formula Let f(x) be the lower limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm. Let f be the upper limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm.

[0023] Step S3, which involves constructing a multi-unit frequency dynamic model considering spatial distribution under frequency disturbances, specifically includes the following steps:

[0024] Based on the original system nodes, synchronous generators and wind farm internal potential nodes are added, resulting in a total of n synchronous generators and wind farm internal potential nodes, n synchronous generators and wind farm terminal nodes, and m load nodes. The nodes are sorted and numbered according to internal potential nodes, terminal nodes, and load nodes.

[0025] Construct a power transfer model between nodes:

[0026] In the formula This represents the power transfer amount between node i and node j. The power self-sharing amount of node i; Let be the initial bus voltage at node i; Let be the initial bus voltage at node j; Let be the electrical conductance between node i and node j; The initial phase angle difference between node i and node j; Let be the susceptance between node i and node j.

[0027] Based on the constructed power transfer model between nodes, the node power transfer matrix is ​​obtained by arranging the nodes in order of their node numbers. :

[0028] In the formula This is the power transfer submatrix between generator nodes; This is the power transfer submatrix between generator nodes and load nodes; This is the power transfer submatrix between load nodes and generator nodes; This is the power transfer submatrix between load nodes;

[0029] Construct the power transfer submatrix from generator node to generator node after equivalent node reduction. for ;

[0030] Construct an equivalent power transfer impact model for each generator node under system frequency disturbance. for ;in, This is the column vector of node power perturbations;

[0031] Based on the frequency response characteristic parameters of each unit, construct the state variable transition matrix under system frequency disturbance. and equivalent power transfer impact matrix :

[0032] In the formula The system frequency before the disturbance; It is an n-dimensional identity diagonal matrix; It is the first intermediate matrix, and , It is a diagonal matrix composed of the inertial time constants of the synchronous generators and wind farms in the system; It is a diagonal matrix formed by the damping coefficients of the synchronous generator and the wind farm in the system, with the damping coefficient of the wind farm being 0. This is the second intermediate matrix, and , This is a diagonal matrix composed of the active power-frequency droop coefficients of the synchronous generators and wind farms in the system. It is a diagonal matrix formed by the reheat coefficients of the synchronous generator and the wind farm in the system, with the reheat coefficient of the wind farm being 0. It is a diagonal matrix formed by the response time constants of the synchronous generator and the wind farm in the system, with the wind farm response time constant being 0.

[0033] Construct a dynamic model of the frequency and rate of change of each node under system frequency perturbation:

[0034] In the formula Let be the vector formed by the deviations of each node frequency from the rated frequency at time t; It is the identity matrix; This represents the characteristic vector of the system's frequency oscillation. This is the characteristic value of the system's frequency oscillation, and , For matrix The kth eigenvalue, where t is the time point in the frequency change process; This is a vector composed of the frequency change rates of each node; Let be the characteristic value of the system's frequency change rate, and .

[0035] The constraints in step S3 specifically include the following steps:

[0036] Construct stability constraints for the frequencies of each node under system frequency perturbations:

[0037] In the formula Let be the rate of change of the frequency of node i at time t; The maximum rate of change of frequency is constrained; Let be the deviation of node i from the rated frequency at time t; This is the maximum deviation constraint between the frequency and the rated frequency.

[0038] Step S4 involves constructing an optimization model for frequency regulation parameters in a wind power system with high penetration rate, taking into account the frequency spatial distribution. This specifically includes the following steps:

[0039] Calculate the regional index for each oscillation mode of the system, and perform matrix calculations. For each conjugate complex eigenvalue, calculate the corresponding left and right eigenvectors; for the matrix... The k-th conjugate complex eigenvalue corresponds to the left and right eigenvectors respectively. and Then the regional index of the k-th oscillation mode Represented as:

[0040] In the formula for The i-th element in; for The i-th element in;

[0041] The oscillation mode c corresponding to the minimum regional exponent is represented by the eigenvalue as follows: ; Calculate the nodal flexibility exponent of oscillation mode c for node i. :

[0042] In the formula Let c be the left eigenvector of c; Let c be the right eigenvector; Let be the load power of node i;

[0043] Based on the node flexibility index of each node, the M nodes with the largest node flexibility index are selected as nodes with severe disturbances; power perturbations are added to these nodes to construct a robust scenario set. ;

[0044] Establish an optimization model for frequency regulation parameters in a wind power-intensive power system that takes into account the spatial distribution of frequency:

[0045] In the formula The objective function value; This is a vector composed of the inertial time constants of each unit in the system; This is a vector composed of the active power-frequency droop coefficients of each unit in the system; For pattern values; It is the second intermediate variable, and , Let be the synchronization cost weighting coefficient for the i-th wind farm. Let i be the frequency of node i at time t. Let be the frequency of the system at time t under the assumption of a center of inertia; It is the first intermediate variable, and , Let be the inertia support compensation price for the i-th wind farm. Let be the inertial time constant of the i-th wind farm. Let be the rate of change of frequency of the i-th wind farm at time t. Let be the primary frequency regulation compensation price for the i-th wind farm.

[0046] This invention also provides a system for optimizing frequency regulation parameters in a high-penetration wind power system, comprising a data acquisition module, a first construction module, a second construction module, a third construction module, and a parameter optimization module; the data acquisition module, the first construction module, the second construction module, the third construction module, and the parameter optimization module are connected in series; the data acquisition module acquires data information of the target power system and uploads the data information to the first construction module; the first construction module constructs a dynamic response model and constraints for each wind farm to provide frequency regulation support under frequency disturbances based on the received and acquired data information, and uploads the data information to the second construction module; the second construction module constructs a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbances based on the received and acquired data information, and uploads the data information to the third construction module; the third construction module constructs a frequency regulation parameter optimization model for a high-penetration wind power system considering frequency spatial distribution based on the received and acquired data information, and uploads the data information to the parameter optimization module; the parameter optimization module solves the constructed model simultaneously based on the received data information to complete the optimization of frequency regulation parameters of the high-penetration wind power system.

[0047] The frequency regulation parameter optimization method and system for high wind power penetration power systems provided by this invention, based on the modeling, disturbance process modeling, and frequency regulation parameter modeling and solving of high wind power penetration power systems, not only achieves the optimization of frequency regulation parameters of high wind power penetration power systems, but also has higher reliability and better accuracy. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0049] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0050] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The frequency regulation parameter optimization method for a wind power high penetration power system disclosed in this invention includes the following steps:

[0051] S1. Obtain data information about the target power system;

[0052] S2. Based on the data obtained in step S1, construct a dynamic response model and constraints for each wind farm to provide frequency regulation support under frequency disturbances; specifically including the following steps:

[0053] Construct dynamic response models for each wind farm to provide frequency regulation support under frequency disturbances:

[0054] Construct dynamic models of wind turbine mechanical power for each wind farm:

[0055] In the formula Let be the mechanical power of the a-th wind turbine in the i-th wind farm at time t; air density; The swept area of ​​the fan blades; Let be the wind speed of the a-th wind turbine in the i-th wind farm; Let be the wind energy capture coefficient of the a-th wind turbine in the i-th wind farm at time t, and , Let be the tip speed ratio of the a-th wind turbine in the i-th wind farm at time t. , Let be the blade radius of the a-th wind turbine in the i-th wind farm. Let be the rotational speed of the a-th wind turbine in the i-th wind farm at time t. Let be the pitch angle of the a-th wind turbine in the i-th wind farm at time t;

[0056] Construct a dynamic model differentiating the speed differences of wind turbines in various wind farms:

[0057] In the formula For discretization time interval; Let be the electromagnetic power of the a-th wind turbine in the i-th wind farm at time t; This refers to the mechanical rotational inertia of the wind turbine blades.

[0058] Construct frequency regulation power allocation models for the frequency regulation support of wind turbines in various wind farms:

[0059] In the formula Let be the frequency regulation margin weighting factor for the a-th wind turbine in the i-th wind farm; Let be the energy state index of the a-th wind turbine in the i-th wind farm at time t, and , Let be the lower limit of the rotational speed of the a-th wind turbine in the i-th wind farm. Let $\frac{a}{i}$ be the upper limit of the rotational speed of the $a$-th wind turbine in the $i$-th wind farm. Let be the number of wind turbines in the i-th wind farm; Let be the inertial time constant of the a-th wind turbine in the i-th wind farm; Let be the total inertial time constant of the i-th wind farm; Let be the total wind turbine capacity of the i-th wind farm; Let a be the rated capacity of the a-th wind turbine in the i-th wind farm; Let be the active-frequency droop coefficient of the a-th wind turbine in the i-th wind farm; is the total active power-frequency droop coefficient for the i-th wind farm; Let be the frequency regulation power undertaken by the a-th wind turbine in the i-th wind farm at time t; Let be the rate of frequency change of the i-th wind farm access node at time t; The system's rated frequency; The frequency deviation between the i-th wind farm access node and the rated frequency at time t; Let be the electromagnetic power output of the a-th wind turbine in the i-th wind farm at time t.

[0060] Construct constraints:

[0061] Establish operational constraints for wind turbines in each wind farm to provide frequency regulation support under frequency disturbances:

[0062] In the formula Let f(x) be the lower limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm. Let f(x) be the upper limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm.

[0063] S3. Based on the data obtained in step S1, construct a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbances; specifically including the following steps:

[0064] Constructing a multi-unit frequency dynamic model considering spatial distribution under frequency disturbances:

[0065] Based on the original system nodes, synchronous generators and wind farm internal potential nodes are added, resulting in a total of n synchronous generators and wind farm internal potential nodes, n synchronous generators and wind farm terminal nodes, and m load nodes. The nodes are sorted and numbered according to internal potential nodes, terminal nodes, and load nodes.

[0066] Construct a power transfer model between nodes:

[0067] In the formula This represents the power transfer amount between node i and node j. The power self-sharing amount of node i; Let be the initial bus voltage at node i; Let be the initial bus voltage at node j; Let be the electrical conductance between node i and node j; The initial phase angle difference between node i and node j; Let be the susceptance between node i and node j.

[0068] Based on the constructed power transfer model between nodes, the node power transfer matrix is ​​obtained by arranging the nodes in order of their node numbers. :

[0069] In the formula This is the power transfer submatrix between generator nodes; This is the power transfer submatrix between generator nodes and load nodes; This is the power transfer submatrix between load nodes and generator nodes; This is the power transfer submatrix between load nodes;

[0070] Construct the power transfer submatrix from generator node to generator node after equivalent node reduction. for ;

[0071] Construct an equivalent power transfer impact model for each generator node under system frequency disturbance. for ;in, This is the column vector of node power perturbations;

[0072] Based on the frequency response characteristic parameters of each unit, construct the state variable transition matrix under system frequency disturbance. and equivalent power transfer impact matrix :

[0073] In the formula The system frequency before the disturbance; It is an n-dimensional identity diagonal matrix; It is the first intermediate matrix, and , It is a diagonal matrix composed of the inertial time constants of the synchronous generators and wind farms in the system; It is a diagonal matrix formed by the damping coefficients of the synchronous generator and the wind farm in the system, with the damping coefficient of the wind farm being 0. This is the second intermediate matrix, and , This is a diagonal matrix composed of the active power-frequency droop coefficients of the synchronous generators and wind farms in the system. It is a diagonal matrix formed by the reheat coefficients of the synchronous generator and the wind farm in the system, with the reheat coefficient of the wind farm being 0. It is a diagonal matrix formed by the response time constants of the synchronous generator and the wind farm in the system, with the wind farm response time constant being 0.

[0074] Construct a dynamic model of the frequency and rate of change of each node under system frequency perturbation:

[0075] In the formula Let be the vector formed by the deviations of each node frequency from the rated frequency at time t; It is the identity matrix; This represents the characteristic vector of the system's frequency oscillation. This is the characteristic value of the system's frequency oscillation, and , For matrix The kth eigenvalue, where t is the time point in the frequency change process; This is a vector composed of the frequency change rates of each node; Let be the characteristic value of the system's frequency change rate, and .

[0076] Construct constraints:

[0077] Construct stability constraints for the frequencies of each node under system frequency perturbations:

[0078] In the formula Let be the rate of change of the frequency of node i at time t; The maximum rate of change of frequency is constrained; Let be the deviation of node i from the rated frequency at time t; This is the maximum deviation constraint between the frequency and the rated frequency.

[0079] S4. Based on the data obtained in step S1, construct an optimization model for frequency regulation parameters of a wind power system with high wind power penetration, taking into account the spatial distribution of frequency; specifically, this includes the following steps:

[0080] Constructing a frequency regulation parameter optimization model for a wind power system with high penetration rate, taking into account the spatial distribution of frequency:

[0081] Calculate the regional index for each oscillation mode of the system, and perform matrix calculations. For each conjugate complex eigenvalue, calculate the corresponding left and right eigenvectors; for the matrix... The k-th conjugate complex eigenvalue corresponds to the left and right eigenvectors respectively. and Then the regional index of the k-th oscillation mode Represented as:

[0082] In the formula for The i-th element in; for The i-th element in;

[0083] The oscillation mode c corresponding to the minimum regional exponent is represented by the eigenvalue as follows: ; Calculate the nodal flexibility exponent of oscillation mode c for node i. :

[0084] In the formula Let c be the left eigenvector of c; Let c be the right eigenvector; Let be the load power of node i;

[0085] Based on the node flexibility index of each node, the M nodes with the largest node flexibility index are selected as nodes with severe disturbances; power perturbations are added to these nodes to construct a robust scenario set. ;

[0086] Establish an optimization model for frequency regulation parameters in a wind power-intensive power system that takes into account the spatial distribution of frequency:

[0087] In the formula The objective function value; This is a vector composed of the inertial time constants of each unit in the system; This is a vector composed of the active power-frequency droop coefficients of each unit in the system; For pattern values; It is the second intermediate variable, and , Let be the synchronization cost weighting coefficient for the i-th wind farm. Let i be the frequency of node i at time t. Let be the frequency of the system at time t under the assumption of a center of inertia; It is the first intermediate variable, and , Let be the inertia support compensation price for the i-th wind farm. Let be the inertial time constant of the i-th wind farm. Let be the rate of change of frequency of the i-th wind farm at time t. Let be the primary frequency regulation compensation price for the i-th wind farm.

[0088] S5. Solve the simultaneous equations of the models constructed in steps S2 to S4 to optimize the frequency regulation parameters of the wind power high penetration power system;

[0089] Using the model constructed in step S4 as the objective function, the models constructed in steps S2 and S3 are solved simultaneously to complete the optimization of frequency regulation parameters for the wind power high penetration power system.

[0090] like Figure 2 The diagram shows the functional modules of the system of the present invention: The system disclosed in this invention for optimizing frequency regulation parameters in a high-penetration wind power system includes a data acquisition module, a first construction module, a second construction module, a third construction module, and a parameter optimization module; the data acquisition module, the first construction module, the second construction module, the third construction module, and the parameter optimization module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the first construction module; the first construction module is used to construct a dynamic response model for each wind farm to provide frequency regulation support under frequency disturbances based on the received data information and the acquired data information. The first module receives and sets constraints, and uploads the data to the second module. The second module, based on the received and acquired data, constructs a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbances, and uploads the data to the third module. The third module, based on the received and acquired data, constructs an optimization model for frequency regulation parameters of a high-penetration wind power system considering frequency spatial distribution, and uploads the data to the parameter optimization module. The parameter optimization module solves the constructed model simultaneously based on the received data to optimize the frequency regulation parameters of the high-penetration wind power system.

Claims

1. A method for optimizing frequency regulation parameters in a wind power-intensive power system, comprising the following steps: S1. Obtain data information about the target power system; S2. Based on the data obtained in step S1, construct a dynamic response model and constraints for each wind farm to provide frequency regulation support under frequency disturbances; S3. Based on the data obtained in step S1, construct a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbances; S4. Based on the data obtained in step S1, construct an optimization model for frequency regulation parameters of a wind power system with high wind power penetration considering frequency spatial distribution; S5. Solve the simultaneous equations of the models constructed in steps S2 to S4 to complete the optimization of frequency regulation parameters for the wind power high penetration power system.

2. The method for optimizing frequency regulation parameters in a high-penetration wind power system according to claim 1, characterized in that... Step S2, which involves constructing a dynamic response model for each wind farm to provide frequency regulation support under frequency disturbances, specifically includes the following steps: Construct dynamic models of wind turbine mechanical power for each wind farm: In the formula Let be the mechanical power of the a-th wind turbine in the i-th wind farm at time t; air density; The swept area of ​​the fan blades; Let be the wind speed of the a-th wind turbine in the i-th wind farm; Let be the wind energy capture coefficient of the a-th wind turbine in the i-th wind farm at time t, and , Let be the tip speed ratio of the a-th wind turbine in the i-th wind farm at time t. , Let be the blade radius of the a-th wind turbine in the i-th wind farm. Let be the rotational speed of the a-th wind turbine in the i-th wind farm at time t. Let be the pitch angle of the a-th wind turbine in the i-th wind farm at time t; Construct a dynamic model differentiating the speed differences of wind turbines in various wind farms: In the formula For discretization time interval; Let be the electromagnetic power of the a-th wind turbine in the i-th wind farm at time t; This refers to the mechanical rotational inertia of the wind turbine blades. Construct frequency regulation power allocation models for the frequency regulation support of wind turbines in various wind farms: In the formula Let be the frequency regulation margin weighting factor for the a-th wind turbine in the i-th wind farm; Let be the energy state index of the a-th wind turbine in the i-th wind farm at time t, and , Let be the lower limit of the rotational speed of the a-th wind turbine in the i-th wind farm. Let $\frac{a}{i}$ be the upper limit of the rotational speed of the $a$-th wind turbine in the $i$-th wind farm. Let be the number of wind turbines in the i-th wind farm; Let be the inertial time constant of the a-th wind turbine in the i-th wind farm; Let be the total inertial time constant of the i-th wind farm; Let be the total wind turbine capacity of the i-th wind farm; Let a be the rated capacity of the a-th wind turbine in the i-th wind farm; Let be the active-frequency droop coefficient of the a-th wind turbine in the i-th wind farm; is the total active power-frequency droop coefficient for the i-th wind farm; Let be the frequency regulation power undertaken by the a-th wind turbine in the i-th wind farm at time t; Let be the rate of frequency change of the i-th wind farm access node at time t; The system's rated frequency; The frequency deviation between the i-th wind farm access node and the rated frequency at time t; Let be the electromagnetic power output of the a-th wind turbine in the i-th wind farm at time t.

3. The method for optimizing frequency regulation parameters in a high-penetration wind power system according to claim 2, characterized in that... The constraints in step S2 specifically include the following steps: Establish operational constraints for wind turbines in each wind farm to provide frequency regulation support under frequency disturbances: In the formula Let f(x) be the lower limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm. Let f be the upper limit of the electromagnetic power of the a-th wind turbine in the i-th wind farm.

4. The method for optimizing frequency regulation parameters in a high-penetration wind power system according to claim 3, characterized in that... Step S3, which involves constructing a multi-unit frequency dynamic model considering spatial distribution under frequency disturbances, specifically includes the following steps: Based on the original system nodes, synchronous generators and wind farm internal potential nodes are added, resulting in a total of n synchronous generators and wind farm internal potential nodes, n synchronous generators and wind farm terminal nodes, and m load nodes. The nodes are sorted and numbered according to internal potential nodes, terminal nodes, and load nodes. Construct a power transfer model between nodes: In the formula This represents the power transfer amount between node i and node j. The power self-sharing amount of node i; Let be the initial bus voltage at node i; Let be the initial bus voltage at node j; Let be the electrical conductance between node i and node j; The initial phase angle difference between node i and node j; Let be the susceptance between node i and node j. Based on the constructed power transfer model between nodes, the node power transfer matrix is ​​obtained by arranging the nodes in order of their node numbers. : In the formula This is the power transfer submatrix between generator nodes; This is the power transfer submatrix between generator nodes and load nodes; This is the power transfer submatrix between load nodes and generator nodes; This is the power transfer submatrix between load nodes; Construct the power transfer submatrix from generator node to generator node after equivalent node reduction. for ; Construct an equivalent power transfer impact model for each generator node under system frequency disturbance. for ;in, This is the column vector of node power perturbations; Based on the frequency response characteristic parameters of each unit, construct the state variable transition matrix under system frequency disturbance. and equivalent power transfer impact matrix : In the formula The system frequency before the disturbance; It is an n-dimensional identity diagonal matrix; It is the first intermediate matrix, and , It is a diagonal matrix composed of the inertial time constants of the synchronous generators and wind farms in the system; It is a diagonal matrix formed by the damping coefficients of the synchronous generator and the wind farm in the system, with the damping coefficient of the wind farm being 0. This is the second intermediate matrix, and , This is a diagonal matrix composed of the active power-frequency droop coefficients of the synchronous generators and wind farms in the system. It is a diagonal matrix formed by the reheat coefficients of the synchronous generator and the wind farm in the system, with the reheat coefficient of the wind farm being 0. It is a diagonal matrix formed by the response time constants of the synchronous generator and the wind farm in the system, with the wind farm response time constant being 0. Construct a dynamic model of the frequency and rate of change of each node under system frequency perturbation: In the formula Let be the vector formed by the deviations of each node frequency from the rated frequency at time t; It is the identity matrix; This represents the characteristic vector of the system's frequency oscillation. This is the characteristic value of the system's frequency oscillation, and , For matrix The kth eigenvalue, where t is the time point in the frequency change process; This is a vector composed of the frequency change rates of each node; Let be the characteristic value of the system's frequency change rate, and .

5. The method for optimizing frequency regulation parameters in a high-penetration wind power system according to claim 4, characterized in that... The constraints in step S3 specifically include the following steps: Construct stability constraints for the frequencies of each node under system frequency perturbations: In the formula Let be the rate of change of the frequency of node i at time t; The maximum rate of change of frequency is constrained; Let be the deviation of node i from the rated frequency at time t; This is the maximum deviation constraint between the frequency and the rated frequency.

6. The method for optimizing frequency regulation parameters in a high-penetration wind power system according to claim 5, characterized in that... Step S4 involves constructing an optimization model for frequency regulation parameters in a wind power system with high penetration rate, taking into account the frequency spatial distribution. This specifically includes the following steps: Calculate the regional index for each oscillation mode of the system, and perform matrix calculations. For each conjugate complex eigenvalue, calculate the corresponding left and right eigenvectors; for the matrix... The k-th conjugate complex eigenvalue corresponds to the left and right eigenvectors respectively. and Then the regional index of the k-th oscillation mode Represented as: In the formula for The i-th element in; for The i-th element in; The oscillation mode c corresponding to the minimum regional exponent is represented by the eigenvalue as follows: ; Calculate the nodal flexibility exponent of oscillation mode c for node i. : In the formula Let c be the left eigenvector of c; Let c be the right eigenvector; Let be the load power of node i; Based on the node flexibility index of each node, the M nodes with the largest node flexibility index are selected as nodes with severe disturbances; power perturbations are added to these nodes to construct a robust scenario set. ; Establish an optimization model for frequency regulation parameters in a wind power-intensive power system that takes into account the spatial distribution of frequency: In the formula The objective function value; This is a vector composed of the inertial time constants of each unit in the system; This is a vector composed of the active power-frequency droop coefficients of each unit in the system; For pattern values; It is the second intermediate variable, and , Let be the synchronization cost weighting coefficient for the i-th wind farm. Let i be the frequency of node i at time t. Let be the frequency of the system at time t under the assumption of a center of inertia; It is the first intermediate variable, and , Let be the inertia support compensation price for the i-th wind farm. Let be the inertial time constant of the i-th wind farm. Let be the rate of change of frequency of the i-th wind farm at time t. Let be the primary frequency regulation compensation price for the i-th wind farm.

7. A system for implementing the frequency regulation parameter optimization method for a wind power high penetration power system as described in any one of claims 1 to 6, characterized in that... It includes a data acquisition module, a first construction module, a second construction module, a third construction module, and a parameter optimization module; the data acquisition module, the first construction module, the second construction module, the third construction module, and the parameter optimization module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the first construction module; the first construction module is used to construct dynamic response models and constraints for each wind farm to provide frequency regulation support under frequency disturbances based on the received data information and the acquired data information, and upload the data information to the second construction module; The second construction module is used to construct a multi-unit frequency dynamic model and constraints considering spatial distribution under frequency disturbance based on the received data information and the acquired data information, and upload the data information to the third construction module; the third construction module is used to construct a frequency regulation parameter optimization model of a wind power high penetration power system considering frequency spatial distribution based on the received data information and the acquired data information, and upload the data information to the parameter optimization module; The parameter optimization module is used to solve the constructed model simultaneously based on the received data information, thereby optimizing the frequency regulation parameters of the wind power high penetration power system.

Images