A new energy grid-connected system modeling method and system

Abstract

The application discloses a new energy grid-connected system modeling method and system, relates to the technical field of power grids, acquires a topological structure of a new energy grid-connected system, constructs a small signal state space model of the topological structure, performs matrix operation on the small signal state space model based on the topological structure, obtains a node voltage observability matrix corresponding to the new energy grid-connected system, performs dominant oscillation mode analysis on the node voltage observability matrix by using each oscillation mode in the small signal state space model, obtains node voltage distribution coefficients corresponding to each node in the topological structure, and constructs an equivalent simplified model corresponding to the new energy grid-connected system according to the node voltage distribution coefficients. The application solves the technical problem that the existing multiple new energy stations interact with each other, affect system dynamic characteristics, cause an equivalent model to be unable to accurately and effectively analyze wide-frequency oscillation in the system, and reduce the accuracy of simulation analysis results of the new energy grid-connected system.

Description

Technical Field

[0001] This invention relates to the field of electrical system modeling and simulation technology, and in particular to a modeling method and system for new energy grid-connected systems. Background Technology

[0002] In recent years, renewable energy power generation technologies such as wind and solar power have developed rapidly and have been widely utilized in power grids. However, broadband oscillations frequently occur in renewable energy grid-connected systems, becoming a significant factor threatening the safe and stable operation of power systems. To study these broadband oscillations, it is necessary to construct models of renewable energy grid-connected systems for theoretical analysis and simulation. However, due to the large number of electrical components involved in these systems, detailed electromagnetic transient modeling and simulation of the entire system is difficult to implement in practice due to limitations in system size and computational speed, making it challenging to conduct a very detailed analysis of its dynamic characteristics. Therefore, equivalent models are typically used to study the dynamic characteristics of renewable energy grid-connected systems.

[0003] Currently, existing equivalent models mainly perform equivalent analysis of the system from the point where multiple power plants are connected to the grid, thereby preserving the dominant oscillation characteristics of the system. However, in scenarios where multiple new energy power plants are connected to the main grid, the interaction between the multiple new energy power plants affects the dynamic characteristics of the system, causing the equivalent model to be unable to accurately analyze the broadband oscillations within the system, thus reducing the reliability of the new energy grid-connected system. Summary of the Invention

[0004] This invention provides a modeling method and system for new energy grid-connected systems, which solves the technical problem that in scenarios where multiple new energy power plants are connected to the main grid, the interaction between multiple new energy power plants affects the dynamic characteristics of the system. Traditional equivalent methods tend to ignore the influence of the characteristics of adjacent power grids on the dominant oscillation mode of the system, resulting in the equivalent model being unable to accurately analyze the broadband oscillations within the system and reducing the accuracy of the analysis results.

[0005] The first aspect of this invention provides a modeling method for a new energy grid-connected system, comprising:

[0006] Obtain the topology of the new energy grid-connected system and construct a small-signal state-space model of the topology;

[0007] Based on the aforementioned topology, matrix operations are performed on the small-signal state-space model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0008] The dominant oscillation mode analysis of the node voltage observability matrix is ​​performed using each oscillation mode in the small-signal state-space model to obtain the node voltage distribution coefficients corresponding to each node in the topology.

[0009] Based on the voltage distribution coefficients of each node, an equivalent simplified model corresponding to the new energy grid-connected system is constructed.

[0010] Optionally, the step of constructing the small-signal state-space model of the topology includes:

[0011] Obtain the power output combination parameters of the topology;

[0012] Based on the state-space representation method, a state-space model is constructed according to the topology and the power output combination parameters.

[0013] The state-space model is linearized to obtain a small-signal state-space model.

[0014] Optionally, the step of performing matrix operations on the small-signal state-space model based on the topology to obtain the node voltage observability matrix corresponding to the new energy grid-connected system includes:

[0015] The state space matrix of the small-signal state space model is diagonalized to obtain the state diagonal matrix and the right eigenvector matrix.

[0016] The right eigenvector matrix is ​​linearly transformed to obtain the modal quantities and the system state vector;

[0017] Based on the system state vector, the topology is discretized and equivalently processed to obtain the system node voltage model;

[0018] By inputting the modal quantities into the system node voltage model, the node voltage observability matrix corresponding to the new energy grid-connected system is obtained.

[0019] Optionally, the step of performing dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small-signal state-space model to obtain the node voltage distribution coefficients corresponding to each node in the topology includes:

[0020] Extract the node voltage vector corresponding to each oscillation mode from the node voltage observability matrix, and solve for the node eigenvalues ​​of each node voltage vector;

[0021] The real part of each node feature value is extracted as the node damping, and the node voltage vector associated with the minimum value of each node damping is selected as the dominant oscillation vector.

[0022] Based on the dominant oscillation vector and the preset voltage oscillation component function, the node voltage distribution coefficients corresponding to each node in the topology are obtained.

[0023] Optionally, the step of obtaining the node voltage distribution coefficients corresponding to each node in the topology based on the dominant oscillation vector and a preset voltage oscillation component function includes:

[0024] By inputting the dominant oscillation vector into a preset voltage oscillation component function, the voltage oscillation components corresponding to each node in the topology are obtained.

[0025] Each voltage oscillation component is processed to obtain the first modulus value corresponding to each node;

[0026] The first mean is obtained by averaging all the first modulus values.

[0027] The first modulus value and the first mean value are compared respectively to obtain the node voltage distribution coefficient corresponding to each node.

[0028] Optionally, the step of constructing an equivalent simplified model of the new energy grid-connected system based on the voltage distribution coefficients of each node includes:

[0029] Determine whether the voltage distribution coefficient of each node is less than a preset risk threshold;

[0030] If the node voltage distribution coefficient is less than the risk threshold, the region associated with the node voltage distribution coefficient is simplified and equalized to obtain an equal node model.

[0031] If the node voltage distribution coefficient is greater than or equal to the risk threshold, then the region associated with the node voltage distribution coefficient is modeled in detail to obtain the target node model.

[0032] By combining the equivalent node models and the target node models, the equivalent simplified model corresponding to the new energy grid connection system is obtained.

[0033] The second aspect of this invention provides a modeling system for a new energy grid-connected system, comprising:

[0034] The acquisition module is used to acquire the topology of the new energy grid-connected system and construct a small-signal state-space model of the topology.

[0035] The mode conversion module is used to perform matrix operations on the small-signal state-space model based on the topology to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0036] The analysis module is used to perform dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small-signal state-space model, and to obtain the node voltage distribution coefficients corresponding to each node in the topology.

[0037] The module is used to construct an equivalent simplified model of the new energy grid-connected system based on the voltage distribution coefficients of each node.

[0038] The third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the new energy grid-connected system modeling method as described in any of the preceding claims.

[0039] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the new energy grid-connected system modeling method as described in any of the preceding claims.

[0040] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the new energy grid-connected system modeling method as described in any of the preceding claims.

[0041] As can be seen from the above technical solutions, the present invention has the following advantages:

[0042] By acquiring the topology of a renewable energy grid-connected system and constructing a small-signal state-space model of the topology, and then performing oscillation mode analysis on the small-signal state-space model, the node voltage distribution coefficients corresponding to each node under the dominant oscillation mode are determined. These node voltage distribution coefficients then guide the construction of an equivalent model for the renewable energy grid-connected system, enabling the equivalent model to accurately reflect the interactions between devices. This overcomes the technical problem that existing methods, such as those involving interactions between multiple renewable energy power plants, affect the system's dynamic characteristics, preventing the equivalent model from accurately analyzing broadband oscillations within the system and reducing the accuracy of operational analysis conclusions for renewable energy grid-connected systems. Compared to traditional equivalent simplification methods, this invention preserves the system's dynamic characteristics, allowing the equivalent model to accurately analyze broadband oscillations within the system and improving the reliability of renewable energy grid-connected system operation. Attached Figure Description

[0043] 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, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1This is a flowchart illustrating the steps of a new energy grid-connected system modeling method provided in Embodiment 1 of the present invention;

[0045] Figure 2 This is a flowchart illustrating the steps of a new energy grid-connected system modeling method provided in Embodiment 2 of the present invention.

[0046] Figure 3 This is a schematic diagram of the structure of the new energy grid-connected system provided in Embodiment 2 of the present invention;

[0047] Figure 4 This is a histogram of node voltage distribution coefficients for heterogeneous oscillation modes in a multi-wind field interval provided in Embodiment 2 of the present invention;

[0048] Figure 5 This is a schematic diagram of the equivalent simplified model provided in Embodiment 2 of the present invention;

[0049] Figure 6 The simulation waveform diagram of the equivalent simplified model provided in Embodiment 2 of the present invention;

[0050] Figure 7 The compass diagram of the output power oscillation component in the equivalent simplified model provided in Embodiment 2 of the present invention;

[0051] Figure 8 This is a structural block diagram of a new energy grid-connected system modeling system provided in Embodiment 3 of the present invention;

[0052] Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0053] This invention provides a modeling method and system for new energy grid-connected systems, which addresses the technical problem that in scenarios where multiple new energy power plants are connected to the main grid, the interaction between these power plants affects the dynamic characteristics of the system. Traditional equivalent methods tend to ignore the influence of adjacent grid characteristics on the dominant oscillation mode of the system, resulting in the equivalent model being unable to accurately analyze broadband oscillations within the system and reducing the accuracy of the analysis results.

[0054] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0055] Please see Figure 1 , Figure 1The flowchart illustrates the steps of a new energy grid-connected system modeling method provided in Embodiment 1 of the present invention.

[0056] This invention provides a modeling method for a new energy grid-connected system, comprising:

[0057] Step 101: Obtain the topology of the new energy grid-connected system and construct a small-signal state-space model of the topology;

[0058] In this embodiment of the invention, the topology of the new energy grid-connected system is obtained, and a small-signal state-space model of the topology is constructed based on the small disturbance analysis method.

[0059] Step 102: Based on the topology, perform matrix operations on the small-signal state-space model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system;

[0060] In this embodiment of the invention, the state space matrix of the small-signal state space model is input into a preset system discrete state space function to obtain modal quantities and system state vectors. The topology is then discretized and equivalently processed based on the system state vectors to obtain the system node voltage model. The modal quantities are then input into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0061] It should be noted that the i-th column in the node voltage observability matrix represents the response of the i-th mode in each node voltage.

[0062] Step 103: Perform dominant oscillation mode analysis on the node voltage observability matrix using the various oscillation modes in the small-signal state-space model to obtain the node voltage distribution coefficients corresponding to each node in the topology.

[0063] Oscillation mode refers to the type of wide-frequency oscillation that occurs in a new energy grid-connected system.

[0064] In this embodiment of the invention, the node voltage vector corresponding to each oscillation mode is extracted from the node voltage observability matrix based on each oscillation mode in the small-signal state-space model. The eigenvalues ​​of each node voltage vector are solved, and the real part of each eigenvalue is extracted as damping. The node voltage vector with the largest damping is selected as the dominant oscillation vector. The dominant oscillation vector is solved according to the preset voltage oscillation component function to obtain the node voltage distribution coefficients corresponding to each node in the topology under the dominant vibration mode.

[0065] Step 104: Construct an equivalent simplified model of the new energy grid-connected system based on the voltage distribution coefficient of each node.

[0066] In this embodiment of the invention, the voltage distribution coefficients of each node are sorted from largest to smallest. The voltage distribution coefficients of nodes with a pre-set modeling threshold are selected as target distribution coefficients, and the remaining node voltage distribution coefficients are used as equivalent distribution coefficients. The intervals associated with each target distribution coefficient are then modeled in detail to obtain a target region model. The regions associated with each equivalent distribution coefficient are then processed to obtain an equivalent region model. The target region model and the equivalent region model are coupled to obtain a simplified equivalent model corresponding to the new energy grid-connected system.

[0067] In this embodiment of the invention, the topology of the new energy grid-connected system is acquired, and a small-signal state-space model of the topology is constructed. Then, oscillation mode analysis is performed on the small-signal state-space model to determine the node voltage distribution coefficients corresponding to each node during the dominant oscillation. These node voltage distribution coefficients guide the construction of an equivalent model for the new energy grid-connected system, enabling the equivalent model to accurately reflect the interactions between devices. This overcomes the technical problem that existing methods involving interactions between multiple new energy power plants affect the system's dynamic characteristics, preventing the equivalent model from accurately analyzing broadband oscillations within the system and reducing the accuracy of operational analysis conclusions for the new energy grid-connected system. Compared with traditional equivalent simplification methods, this invention preserves the system's dynamic characteristics, allowing the equivalent model to accurately analyze broadband oscillations within the system and improving the reliability of the new energy grid-connected system's operation.

[0068] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a new energy grid-connected system modeling method provided in Embodiment 2 of the present invention.

[0069] This invention provides a modeling method for a new energy grid-connected system, comprising:

[0070] Step 201: Obtain the topology of the new energy grid-connected system and construct a small-signal state-space model of the topology;

[0071] Further, step 201 includes the following sub-steps:

[0072] S11. Obtain the power output combination parameters of the topology;

[0073] Voltage and output combination parameters refer to the capacity and output parameters of different types of power sources in the power grid of each region in the new energy grid-connected system.

[0074] In this embodiment of the invention, the power capacity and output parameters of different types of power sources in each region of the topology are obtained.

[0075] S12. Based on the state-space representation method, construct a state-space model according to the topology and power output combination parameters;

[0076] In this embodiment of the invention, a state-space model of the topology is constructed based on the state-space representation and the power output combination parameters.

[0077] It is worth mentioning that state-space representation can be used to describe various types of dynamic systems, including linear and nonlinear systems, time-invariant and time-varying systems, single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems. For example, it can be used to describe simple circuit systems, mechanical systems, or complex aerospace control systems.

[0078] S13. Linearize the state-space model to obtain the small-signal state-space model.

[0079] In this embodiment of the invention, the state-space model is linearized to obtain a small-signal state-space model.

[0080] Step 202: Based on the topology, perform matrix operations on the small-signal state-space model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system;

[0081] Further, step 202 includes the following sub-steps:

[0082] S21. Diagonalize the state space matrix of the small signal state space model to obtain the state diagonal matrix and the right eigenvector matrix.

[0083] In this embodiment of the invention, the state space matrix of the small signal state space model is input into a preset diagonalization decomposition function to obtain the state diagonal matrix and the right eigenvector matrix.

[0084] It should be noted that the diagonalization decomposition function is specifically:

[0085]

[0086] V T W=I

[0087] Where V is the left eigenvector matrix corresponding to the discrete eigenvalues, and W is the right eigenvector matrix corresponding to the discrete eigenvalues. for, Let T be the state space matrix, T be the transpose of the matrix, and I be the identity matrix with the same dimension as the state space matrix.

[0088] S22. Perform a linear transformation on the right eigenvector matrix to obtain the modal quantities and the system state vector;

[0089] Modal variables refer to system state variables.

[0090] In this embodiment of the invention, the right eigenvector matrix is ​​input into a preset linear transformation function to obtain the modal quantities and the system state vector.

[0091] It should be noted that the linear transformation function is specifically as follows:

[0092]

[0093] in, Let be the system state vector. It is a modal quantity.

[0094] S23. Based on the system state vector, the topology is discretized and equivalently processed to obtain the system node voltage model;

[0095] In this embodiment of the invention, a node voltage function is constructed based on the topology. An injection current function is constructed using the branch-node correlation matrix and the historical current terms in the discrete model of each component. The node voltage function and the injection current function are combined to obtain the initial system node voltage model. The system state vector is input into the initial system node voltage model to obtain the system node voltage model.

[0096] It is worth mentioning that, since an AC system can be represented in the discrete domain as a discrete equivalent circuit network consisting of the equivalent conductance of components and historical current sources, this network can be described by nodal voltage equations (i.e., nodal voltage functions). Based on the interconnection structure between the components in the system, the nodal injected current in the nodal voltage equations can be expressed as the product of the branch-node correlation matrix and the historical current term in the discrete model of each component (i.e., the injected current function).

[0097] It should be noted that the node voltage function is as follows:

[0098]

[0099] in, For the equivalent conductance of the component, Inject current into the node, This represents the system node voltage.

[0100] It should be noted that the injected current function is as follows:

[0101]

[0102] in, Let T be a diagonal block matrix composed of the coefficient matrices from the historical current sources of each element, and let T be the transpose of the matrix. This is the coefficient matrix of the historical current sources for each component. It is the product of the diagonal block matrix formed by the coefficient matrices of the historical current sources of each component and the system state vector.

[0103] It should be noted that the specific system node voltage model is as follows:

[0104]

[0105] in, Let be the node voltage observability matrix.

[0106] S24. Input the modal quantities into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0107] In this embodiment of the invention, the modal quantities are used as inputs to the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0108] Step 203: Extract the node voltage vectors corresponding to each oscillation mode from the node voltage observability matrix, and solve for the node eigenvalues ​​of each node voltage vector;

[0109] In this embodiment of the invention, the node voltage observability matrix is ​​divided into multiple node voltage vectors according to each oscillation mode, and the node voltage vectors are solved to obtain the node eigenvalues ​​of each node voltage vector.

[0110] It is worth mentioning that since the modal quantities of the system represented by Z(t) are decoupled from each other, the i-th column of matrix S characterizes the response of the i-th mode in each node voltage. Since the node voltages and branch currents are converted to a unified xy rotating coordinate system for the entire network when modeling the state space of the whole system, the node voltage vector in the injected current function should be of order 2n×1 (including the x / y axis components of the voltage) for an n-node system. Therefore, in order to obtain the total response of the oscillating components in each node voltage, it is necessary to superimpose the x-axis components and y-axis components.

[0111] Step 204: Extract the real part of the eigenvalues ​​of each node as the node damping, and select the node voltage vector associated with the minimum value of each node damping as the dominant oscillation vector.

[0112] In this embodiment of the invention, the real part of the feature value of each node is extracted as the node damping, and the node voltage vector associated with the minimum value among all node dampings is selected as the dominant oscillation vector.

[0113] Step 205: Based on the dominant oscillation vector and the preset voltage oscillation component function, obtain the node voltage distribution coefficients corresponding to each node in the topology.

[0114] Furthermore, step 205 includes the following sub-steps:

[0115] S31. Input the dominant oscillation vector into the preset voltage oscillation component function to obtain the voltage oscillation components corresponding to each node in the topology.

[0116] In this embodiment of the invention, the dominant vibration line is used as the input of a preset voltage oscillation component function to obtain the voltage oscillation components corresponding to each node in the topology.

[0117] It should be noted that the voltage oscillation component function is specifically as follows:

[0118]

[0119] in, Let represent the voltage oscillation component of the p-th node in the i-th mode, where p is the oscillation mode number and i is the node number. It is the imaginary unit.

[0120] S32. Perform modulus processing on each voltage oscillation component to obtain the first modulus value corresponding to each node;

[0121] In this embodiment of the invention, each voltage oscillation component is input into a preset modulus function to obtain the first modulus value corresponding to each node.

[0122] S33. Average all the first modulus values ​​to obtain the first mean;

[0123] S34. Ratio each first modulus value with the first mean value to obtain the node voltage distribution coefficient corresponding to each node.

[0124] In this embodiment of the invention, each voltage oscillation component is input into a preset node voltage distribution coefficient function to obtain the node voltage distribution coefficient corresponding to each node.

[0125] It should be noted that the node voltage distribution coefficient function is specifically as follows:

[0126]

[0127] in, Let be the node voltage distribution coefficient of the i-th mode at node p, j be the node number of the i-th mode, and n be the total number of nodes of the i-th mode.

[0128] Step 206: Construct an equivalent simplified model of the new energy grid-connected system based on the voltage distribution coefficient of each node.

[0129] Furthermore, step 206 includes the following sub-steps:

[0130] S41. Determine whether the voltage distribution coefficient of each node is less than the preset risk threshold.

[0131] It should be noted that the voltage distribution coefficients of each node are sorted, and the distribution coefficient of the Qth node is selected as the risk threshold.

[0132] S42. If the node voltage distribution coefficient is less than the risk threshold, the region associated with the node voltage distribution coefficient is simplified and equalized to obtain an equivalent node model.

[0133] In this embodiment of the invention, it is determined whether the voltage distribution coefficient of each node is less than a preset risk threshold. When the node voltage distribution coefficient is less than the risk threshold, the region associated with the node voltage distribution coefficient is simplified and equalized to obtain an equivalent node model (that is, the region with a smaller node voltage distribution coefficient is simplified and equalized).

[0134] S43. If the node voltage distribution coefficient is greater than or equal to the risk threshold, then the region associated with the node voltage distribution coefficient will be modeled in detail to obtain the target node model.

[0135] In this embodiment of the invention, when the node voltage distribution coefficient is greater than or equal to the risk threshold, the region associated with the node voltage distribution coefficient is modeled in detail to obtain the target node model (that is, the region with a large node voltage distribution coefficient is modeled in detail).

[0136] S44. Combine the equivalent node models and the target node models to obtain the equivalent simplified model corresponding to the new energy grid connection system.

[0137] In this embodiment of the invention, each equivalent node model and each target node model are coupled to obtain an equivalent simplified model corresponding to the new energy grid connection system.

[0138] In another embodiment, see Figure 3As shown, the topology and multiple oscillation modes of the new energy grid-connected system are obtained. 1. The new energy grid-connected system is based on a 500kV backbone network and is mainly divided into five regions. Region 1 and Region 4, and Region 2 and Region 3 are interconnected through back-to-back flexible DC line 1 (VSC-HVDC1) and back-to-back flexible DC line 2 (VSC-HVDC2), respectively. Other regions are interconnected through the 500kV AC large loop line indicated by the red line. 2. In each region's power grid, a large-capacity equivalent thermal power unit is used to simulate a conventional synchronous machine power source. Only the 500kV lines between the main grid represented by the equivalent machine and each external connection node are retained within the region. 3. The example includes 6 conventional DC lines and 1 flexible DC line. The DC capacity and connection location are LCC-HVDC1 (±800kV, 5000MW, connection area 1), LCC-HVDC2 (±500kV, 3000MW, connection area 2), LCC-HVDC3 (±500kV, 6400MW, double circuit, connection area 2), LCC-HVDC4 (±500kV, 3000MW, connection area 3), LCC-HVDC5 (±800kV, 5000MW, connection area 3), LCC-HVDC6 (±800kV, 5000MW, connection area 4) and VSC-HVDC3 (±400kV, 5000MW, connection area 3). 4. The example includes 4 direct-drive wind farms and 2 onshore photovoltaic power stations. Wind farm 2 (4000MW) and wind farm 3 (3000MW) are connected to area 1, wind farm 1 (6000MW) and photovoltaic power station 1 (3000MW) are connected to area 2, wind farm 4 (4000MW) is connected to area 4, and photovoltaic power station 2 (2000MW) is connected to area 3. Each new energy power station is equipped with a 20% dynamic reactive power compensation device (SVG) according to the regulations.

[0139] First, a small-signal state-space model needs to be constructed for the simulation system, and the capacity and output of different types of power sources in each regional power grid are set as shown in Table 1.

[0140] Table 1

[0141]

[0142] In the above scenario, asynchronous power sources (including new energy power generation and conventional / flexible DC feeds) account for 64% of the total installed power capacity in the system, and the output of asynchronous power sources accounts for 72% of the total load capacity. After establishing the small-signal state-space model of the example system under this operating scenario, the eigenvalues ​​of the state-space matrix are solved. The calculation results of some node damping are shown in Table 2. The dynamic characteristics of the system will be dominated by different node damping modes.

[0143] Table 2

[0144]

[0145] Analysis of the participation factors and component participation of each eigenvalue in Table 2 shows that the modal λ 1,2 , λ 9,10 , λ 11,12 , λ 13,14 For the electromechanical oscillation mode corresponding to the synchronous machine, mode λ 3,4 , λ 7,8 , λ 15,16 For the subsynchronous oscillation mode related to the current loop control of the direct-drive wind farm, mode λ 17,18 The results indicate that the supersynchronous oscillation modes associated with the control of the VSC-HVDC1 and VSC-HVDC3 receiver-end converters are defined. These results suggest that when the proportion of power electronic devices is high, Figure 3 The system shown may face three typical oscillation instability risks: synchronous machine-dominated electromechanical oscillation, wind field-dominated subsynchronous oscillation, and flexible DC-dominated supersynchronous oscillation.

[0146] However, the subsynchronous oscillation modes, which are associated with three pairs of nodes with relatively small positive damping ratios in the direct-drive wind farm, will further weaken when a fault occurs. The damping ratios of these modes may become negative, leading to oscillation problems. Subsequently, this was addressed by implementing fully connected, N-1, and N-2 methods (N-1 being the disconnection of one of the two external transmission lines of wind farm 1...). Figure 2 The characteristic values ​​corresponding to the two operating modes are recalculated based on the B6-B326 line of the middle bus, and N-2 is the result of disconnecting one of the two transmission lines of the wind farm 2 (i.e., the B2-B327 line) on the basis of N-1. The three pairs of oscillation modes related to the direct-drive wind farm are listed in Table 3.

[0147] Table 3

[0148]

[0149] To ensure that the simplified equivalent model of the power grid accurately reflects the dynamic characteristics of the dominant oscillation mode in the complete system, it is crucial to retain the power grid structure that significantly influences the dominant oscillation mode. This invention sorts the voltage distribution coefficients of each node from largest to smallest, selects the voltage distribution coefficients of nodes with a pre-set modeling threshold as target distribution coefficients, and uses the remaining node voltage distribution coefficients as equivalent distribution coefficients. The intervals associated with each target distribution coefficient are then modeled in detail to obtain a target region model. The regions associated with each equivalent distribution coefficient are then processed to obtain an equivalent region model. Coupled with the target region model and the equivalent region model, a simplified equivalent model corresponding to the new energy grid-connected system is obtained.

[0150] In the N-2 mode, three dominant instability modes exist simultaneously across multiple wind fields: regional heterogeneous oscillations, intra-regional heterogeneous oscillations, and common-mode oscillations. The regional heterogeneous mode with the largest real part, λ...3,4 Taking (6.38±j35.3×2π) as an example, calculate its distribution coefficient in all node voltages of the system, and arrange the results in descending order, such as... Figure 4 As shown in the figure, the two nodes with the largest distribution coefficients are B299 and B300. Taking 1 / 10 of the largest distribution coefficient as the limit (i.e., the red line marked in the magnified part of the figure), it is believed that nodes with distribution coefficients greater than this limit have a greater impact on the dynamics of mode λ3,4, and are retained in the modeling.

[0151] according to Figure 4 The results show that there are 20 nodes with distribution coefficients exceeding 1 / 10 of the maximum value, which are listed in Table 4. Similarly, the node voltage distribution coefficients for the other two oscillating instability modes are calculated, and the nodes with a greater impact on the two modes are identified by using 1 / 10 of the maximum value as the boundary, and are listed in Table 4.

[0152] Table 4

[0153]

[0154] Connect the key nodes in Table 4 with... Figure 3 A comparison of the system node numbers reveals that, although the key nodes differ across modes, they share the common characteristic of being distributed simultaneously in both Region 1 and Region 2 power grids, rather than just within the vicinity of a single wind farm. This result indicates that, for the three wind farm-dominated modes, coupling exists between Region 1 and Region 2 power grids due to grid structure characteristics and interactions between wind farms. Therefore, the conventional engineering practice of extending three nodes outward from the location of new energy access to the grid and simplifying the equivalent values ​​of the two regional power grids separately is inappropriate.

[0155] To obtain a simplified power grid model that simultaneously preserves the oscillation characteristics of the above three modes, the union of the nodes in Table 4 is taken, and these nodes and the power grid range they cover are modeled in detail, resulting in... Figure 5 The simplified model shown. (Compared to...) Figure 3 Compared to the complete system model, this simplified model retains the two regional power grid structures connected to wind farms 1 / 2 / 3, and equates the remaining system at AC external connection node B9 and the AC receiving node of the flexible DC transmission line.

[0156] Keeping the equipment parameters and system power flow in the simplified model consistent with the detailed model, we will still use the N-2 method as an example to calculate the eigenvalues ​​of the simplified system. The three pairs of oscillation modes related to the wind field are shown in Table 5.

[0157] Table 5

[0158]

[0159] A comparison with the eigenvalue results obtained from the detailed system model in Table 3 shows that they are basically the same, indicating that the method used... Figure 5 The simplified model can still accurately analyze the multimodal oscillation problem caused by the complex interaction between multiple wind fields. Furthermore, the analysis of the oscillation patterns of each mode shows that heterogeneous mode oscillation is still the main mode among multiple wind fields in the simplified model, which is consistent with the relevant analysis conclusions in the detailed model.

[0160] To verify the effectiveness of the simplified equivalent method, a detailed electromagnetic transient simulation model of the system in this example was established in PSCAD / EMTDC. At 2 seconds, one of the power transmission lines to each of the three wind farms was disconnected. The output power waveforms and their spectral analysis results for the three wind farms are as follows: Figure 6 As shown, all three wind fields exhibited significant oscillations, with the waveforms containing three oscillation frequency components: 31.5Hz, 32.7Hz, and 35.1Hz. The amplitude and phase of each wind field oscillation component were plotted in compass form, as shown below. Figure 7 As shown. Simulation results indicate that for the 35.1Hz component, wind field 1 and wind field 2 / 3 oscillations are out of phase, corresponding to the heterogeneous oscillation mode λ in the corresponding interval. 3,4 For the 32.7Hz component, the 2 / 3 amplitude of the wind field is relatively large and the phase is opposite, corresponding to the heterogeneous oscillation mode λ in the region. 7,8 For the 31.5Hz component, the 1 / 2 / 3 oscillation phases of the wind field are basically the same, corresponding to the common-mode oscillation mode λ. 15,16 The simulation results are consistent with the modal analysis results.

[0161] In summary, when analyzing the multimodal oscillation problem of the system in the scenario of multiple wind farms connected to the main grid, the method of determining the equivalent range of the power grid based on the node voltage distribution coefficient can effectively preserve the dominant oscillation characteristics of the system. The key is to preserve the grid structure that has a significant impact on the dominant instability mode and the interaction path between wind farms related to it.

[0162] In this embodiment of the invention, the topology of the new energy grid-connected system is acquired, and a small-signal state-space model of the topology is constructed. Then, oscillation mode analysis is performed on the small-signal state-space model to determine the node voltage distribution coefficients corresponding to each node during the dominant oscillation. These node voltage distribution coefficients guide the construction of an equivalent model for the new energy grid-connected system, enabling the equivalent model to accurately reflect the interactions between devices. This overcomes the technical problem that existing methods involving interactions between multiple new energy power plants affect the system's dynamic characteristics, preventing the equivalent model from accurately analyzing broadband oscillations within the system and reducing the accuracy of operational analysis conclusions for the new energy grid-connected system. Compared with traditional equivalent simplification methods, this invention preserves the system's dynamic characteristics, allowing the equivalent model to accurately analyze broadband oscillations within the system and improving the reliability of the new energy grid-connected system's operation.

[0163] Please see Figure 8 , Figure 8 This is a structural block diagram of a new energy grid-connected system modeling system provided in Embodiment 3 of the present invention.

[0164] This invention provides a modeling system for a new energy grid-connected system, comprising:

[0165] The acquisition module 301 is used to acquire the topology of the new energy grid-connected system and construct a small-signal state-space model of the topology.

[0166] The mode conversion module 302 is used to perform matrix operations on the small-signal state-space model based on the topology to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0167] Analysis module 303 is used to perform dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small-signal state-space model, and to obtain the node voltage distribution coefficients corresponding to each node in the topology.

[0168] Module 304 is used to construct an equivalent simplified model of the new energy grid-connected system based on the voltage distribution coefficients of each node.

[0169] Furthermore, the acquisition module 301 includes:

[0170] The acquisition submodule is used to obtain the power output combination parameters of the topology;

[0171] A submodule is constructed to build a state-space model based on the state-space representation method, according to the topology and power output combination parameters.

[0172] The linearization submodule is used to linearize the state-space model to obtain a small-signal state-space model.

[0173] Furthermore, the mode conversion module 302 includes:

[0174] The decomposition submodule is used to diagonalize the state space matrix of the small-signal state space model to obtain the state diagonal matrix and the right eigenvector matrix.

[0175] The linear transformation submodule is used to perform a linear transformation on the right eigenvector matrix to obtain the modal quantities and the system state vector.

[0176] The discrete equivalent submodule is used to perform discrete equivalent processing on the topology based on the system state vector to obtain the system node voltage model.

[0177] The second configuration submodule is used to input modal quantities into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0178] Furthermore, the analysis module 303 includes:

[0179] The feature extraction submodule is used to extract the node voltage vectors corresponding to each oscillation mode from the node voltage observability matrix and solve for the node eigenvalues ​​of each node voltage vector.

[0180] The damping submodule is used to extract the real part of the eigenvalues ​​of each node as node damping, and select the node voltage vector associated with the minimum value of each node damping as the dominant oscillation vector.

[0181] The oscillation analysis submodule is used to obtain the node voltage distribution coefficients corresponding to each node in the topology based on the dominant oscillation vector and the preset voltage oscillation component function.

[0182] Furthermore, the oscillation analysis submodule includes:

[0183] The analysis unit is used to input the dominant oscillation vector into a preset voltage oscillation component function to obtain the voltage oscillation components corresponding to each node in the topology.

[0184] The modulus unit is used to perform modulus processing on each voltage oscillation component to obtain the first modulus corresponding to each node.

[0185] The mean value unit is used to perform mean processing on all the first modulus values ​​to obtain the first mean value;

[0186] The ratio unit is used to perform ratio processing on each first modulus value and the first mean value to obtain the node voltage distribution coefficient corresponding to each node.

[0187] Furthermore, module 304 is constructed, including:

[0188] The first analysis submodule is used to determine whether the voltage distribution coefficient of each node is less than the preset risk threshold.

[0189] If the node voltage distribution coefficient is less than the risk threshold, the region associated with the node voltage distribution coefficient is simplified and equalized to obtain an equivalent node model.

[0190] If the node voltage distribution coefficient is greater than or equal to the risk threshold, then the region associated with the node voltage distribution coefficient is modeled in detail to obtain the target node model.

[0191] The simultaneous equation submodule is used to combine the equivalent node models and the target node models to obtain the equivalent simplified model corresponding to the new energy grid-connected system.

[0192] Please see Figure 9 , Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0193] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 402 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the new energy grid-connected system modeling method as described in any of the above embodiments.

[0194] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above.

[0195] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the new energy grid-connected system modeling method as described in any of the above embodiments.

[0196] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the new energy grid-connected system modeling method as described in any of the above embodiments.

[0197] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0199] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0200] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0201] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0202] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling a new energy grid-connected system, characterized in that, The method comprises the following steps: acquiring a topology structure of a new energy grid-connected system, and constructing a small signal state space model of the topology structure; based on the topology structure, performing matrix operation on the small signal state space model to obtain a node voltage observability matrix corresponding to the new energy grid-connected system; using each oscillation mode in the small signal state space model to perform dominant oscillation mode analysis on the node voltage observability matrix to obtain a node voltage distribution coefficient corresponding to each node in the topology structure; constructing an equivalent simplified model corresponding to the new energy grid-connected system according to each node voltage distribution coefficient; the step of constructing the equivalent simplified model corresponding to the new energy grid-connected system according to each node voltage distribution coefficient comprises: determining whether each node voltage distribution coefficient is less than a preset risk threshold value, wherein each node voltage distribution coefficient is sorted, and the Qth node distribution coefficient is selected as the risk threshold value; if the node voltage distribution coefficient is less than the risk threshold value, the region associated with the node voltage distribution coefficient is simplified and equivalent processed to obtain an equivalent node model; if the node voltage distribution coefficient is greater than or equal to the risk threshold value, the region associated with the node voltage distribution coefficient is detailed modeled to obtain a target node model; each equivalent node model and each target node model are combined to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

2. The method of claim 1, wherein, The step of constructing the small signal state space model of the topology structure comprises: acquiring power output combination parameters of the topology structure; based on state space representation, constructing a state space model according to the topology structure and the power output combination parameters; performing linearization processing on the state space model to obtain a small signal state space model.

3. The method of claim 1, wherein, The step of performing matrix operation on the small signal state space model based on the topology structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system comprises: performing diagonalization decomposition on the state space matrix of the small signal state space model to obtain a state diagonal matrix and a right eigenvector matrix; performing linear transformation on the right eigenvector matrix to obtain modal quantities and system state vectors; performing discrete equivalent processing on the topology structure according to the system state vectors to obtain a system node voltage model; inputting the modal quantities into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

4. The method of claim 1, wherein, The step of using each oscillation mode in the small signal state space model to perform dominant oscillation mode analysis on the node voltage observability matrix to obtain a node voltage distribution coefficient corresponding to each node in the topology structure comprises: extracting a node voltage vector corresponding to each oscillation mode from the node voltage observability matrix, and solving a node eigenvalue of each node voltage vector; respectively extracting a real part of each node eigenvalue as a node damping, and selecting a node voltage vector associated with the minimum value of each node damping as a dominant oscillation vector; According to the dominant oscillation vector and a preset voltage oscillation component function, a node voltage distribution coefficient corresponding to each node in the topology structure is obtained.

5. The method of claim 4, wherein, The step of obtaining the node voltage distribution coefficient corresponding to each node in the topology structure according to the dominant oscillation vector and the preset voltage oscillation component function comprises: The dominant oscillation vector is input into the preset voltage oscillation component function to obtain a voltage oscillation component corresponding to each node in the topology structure; Each voltage oscillation component is subjected to a modulus processing to obtain a first modulus value corresponding to each node; All the first modulus values are subjected to a mean value processing to obtain a first mean value; Each first modulus value is subjected to a ratio processing with the first mean value to obtain a node voltage distribution coefficient corresponding to each node.

6. A new energy grid-connected system modeling system, characterized in that, The new energy grid-connected system modeling method of claim 1 is executed, and the system comprises: A collection module is configured to acquire a topology structure of a new energy grid-connected system and construct a small signal state space model of the topology structure; A modal conversion module is configured to perform matrix operation on the small signal state space model based on the topology structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system; An analysis module is configured to perform dominant oscillation mode analysis on the node voltage observability matrix by using each oscillation mode in the small signal state space model to obtain a node voltage distribution coefficient corresponding to each node in the topology structure; A construction module is configured to construct an equivalent simplified model corresponding to the new energy grid-connected system according to each node voltage distribution coefficient.

7. An electronic device, comprising: A computer program is stored in a memory and executed by a processor, so that the processor executes the steps of the new energy grid-connected system modeling method of any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the new energy grid-connected system modeling method of any one of claims 1-5.

9. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the new energy grid-connected system modeling method of any one of claims 1-5.

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