Power system inertia distribution optimization method and device based on node inertia and medium

Abstract

The invention discloses a power system inertia distribution optimization method and device based on node inertia and a medium. The method comprises the following steps: setting basic parameters; taking minimization of system inertia total cost as a target, and considering upper and lower limit constraints of an equivalent inertia constant of the new energy grid-connected converter, a system frequency change rate constraint and a system inertia space distribution constraint to construct an optimization model M1; solving the optimization model M1 to obtain the total inertia cost of the system; taking maximization of the overall inertia level of the system as a target, and considering upper and lower limit constraints of an equivalent inertia constant of the new energy grid-connected converter, a system frequency change rate constraint, a system inertia space distribution constraint and a system inertia total cost constraint to construct an optimization model M2; and solving the optimization model M2, and outputting the value of the equivalent inertia constant of the new energy grid-connected converter. According to the method, the frequency change rate constraint and the inertia space distribution constraint are considered, efficient solution of inertia distribution optimization of the power system is realized, and the method can be used for improving the frequency stability of the power system.

Description

Technical Field

[0001] The present invention belongs to the field of power system inertia, and in particular relates to a method, a device and a medium for optimizing the distribution of inertia of a power system based on node inertia. Background Art

[0002] After a disturbance occurs in the power system, a large frequency change rate can cause synchronous generator damage, new energy equipment disconnection from the grid, and even system frequency collapse, which can affect the stability of the system frequency. System inertia plays a key role in the early stages of a disturbance. Insufficient inertia will directly lead to an over-limit frequency change rate. Therefore, the evaluation and optimization of system inertia is the key to ensuring frequency stability.

[0003] In systems with a high proportion of renewable energy, the inertia control methods of the converter (such as virtual inertia control, virtual synchronous generator control, etc.) and their parameter differences lead to uneven distribution of system inertia. During power disturbances, the greater the difference in system inertia, the more violent the frequency oscillation. Therefore, significant spatial distribution differences in system inertia should be avoided. At the same time, the intermittent and volatile nature of renewable energy generation causes frequent changes in equipment status, resulting in obvious time-varying characteristics of system inertia. A fast and accurate inertia distribution optimization method is urgently needed to meet real-time needs.

[0004] In the prior art, for example, Chinese invention patent document CN116885733A discloses "a method and device for optimizing operation of a power system based on inertia demand evaluation", the method comprising: respectively calculating the inertia demand based on the frequency change rate constraint and the inertia demand based on the frequency maximum offset constraint, and taking the adjustable power as a constraint; establishing a power system inertia demand evaluation model with the goal of minimizing the inertia required by the power system; based on the power system inertia demand evaluation model, calculating the inertia demand of the power system under a preset power disturbance at a preset number of moments according to a genetic algorithm, and solving the system optimization model with the calculated inertia demand as a constraint to generate an optimized operation strategy for each unit in the power system.

[0005] Most of the existing technologies deduce the inertia requirements in the optimization of the inertia distribution of the power system from the inertia center or frequency equivalent model of the synchronous generator, which cannot accurately evaluate the spatial distribution characteristics of the system inertia, greatly reducing the accuracy of the optimization results of the inertia distribution of the power system. Some technologies also use time domain simulation technology to analyze the inertia requirements in the optimization of the inertia distribution of the power system, but with the expansion of the system scale and the increase in the proportion of new energy, the dimension of the system model increases sharply, the required calculation step size is smaller, and the speed of the time domain simulation calculation decreases sharply. Therefore, the time domain simulation calculation faces a contradiction between accuracy and speed, and it is difficult to meet the requirements of real-time optimization of the system's time-varying inertia. Summary of the invention

[0006] In order to solve at least one of the problems existing in the prior art, the present invention provides a method, device and medium for optimizing the inertia distribution of a power system based on node inertia to evaluate the spatial distribution characteristics of the system inertia.

[0007] In order to achieve the purpose of the present invention, the present invention provides a method for optimizing the inertia distribution of a power system based on node inertia, wherein the power system includes a plurality of nodes, and the optimization method includes the following steps: S1. Set the basic parameters of the optimization method, including the system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia .

[0008] S2. With the goal of minimizing the total cost of system inertia, the equivalent inertia constants of all renewable energy grid-connected converters in the power system are taken as decision variables. The optimization model M1 is constructed by considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints, and the system inertia spatial distribution constraints.

[0009] The objective function expression in the optimization model M1 is as follows:

[0010] in, represents the total system inertia cost; A collection of new energy grid-connected converters; Grid-connected converter for new energy inertia cost.

[0011] S3. Use the optimization algorithm to solve the optimization model M1 and obtain the equivalent inertia constant set of all new energy grid-connected converters in the system. and total system inertia cost .

[0012] S4. With the goal of maximizing the overall inertia level of the system, the equivalent inertia constants of all renewable energy grid-connected converters in the power system are taken as decision variables. The optimization model M2 is constructed by considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints, the system inertia spatial distribution constraints and the system inertia total cost constraints.

[0013] The objective function expression in the optimization model M2 is as follows:

[0014] in, Represents the overall inertia level of the system.

[0015] Among them, the constraint expressions in the optimization model M1 and the optimization model M2 are: 1) The expression of the upper and lower limit constraints of the equivalent inertia constant of the new energy grid-connected converter is:

[0016] in, For new energy grid-connected converter The equivalent inertia constant of and Respectively, new energy grid-connected converter The minimum and maximum values ​​of the equivalent inertia constant are related to factors such as the control mode, overcurrent capacity and energy storage device of the equipment; 2) The expression of the system frequency change rate constraint is:

[0017] in, For Node The nodal inertia coefficient of To meet the minimum value of node inertia coefficient under the system frequency change rate constraint requirement; is the number of nodes in the power system; 3) The expression of the spatial distribution constraint of system inertia is:

[0018] in, is the spatial distribution index of system inertia; 4) The expression of the total cost constraint of system inertia is:

[0019] S5. The equivalent inertia constants of all new energy grid-connected converters in the power system obtained by the optimization model M1 are set As the new energy grid-connected converter in the optimization model M2 The equivalent inertia constant The optimization algorithm is used to solve the optimization model M2, and the value of the equivalent inertia constant of the optimized new energy grid-connected converter is output.

[0020] Furthermore, new energy grid-connected converters Inertia cost The calculation formula is:

[0021] in, , , and All are new energy grid-connected converters The inertia cost coefficient of the equivalent inertia constant; For new energy grid-connected converter The inertia cost threshold for the equivalent inertia constant.

[0022] Furthermore, the node The node inertia coefficient The calculation formula is:

[0023] in, is the number of devices in the power system; For equipment in power systems Rated capacity; For equipment in power systems The equivalent inertia constant of Augmenting the admittance matrix for power systems No. Line Column elements.

[0024] Furthermore, the system augmented admittance matrix The calculation formula is:

[0025] in, is the imaginary part of the node admittance matrix; Indicates the size of the equivalent reactance inside the device. The diagonal elements of are the negative values ​​of the inverse of the internal equivalent reactance of the equipment, and the off-diagonal elements are 0; Indicates the connection relationship between the device and the power grid. Connect at the node ,So No. Line The elements of the column are devices The reciprocal of the internal equivalent reactance.

[0026] Furthermore, the equipment in the power system The equivalent inertia constant is a real number greater than or equal to 0. If the device is a synchronous generator, then the equivalent inertia constant of the synchronous generator is the inertia constant of the synchronous generator. If the device is a new energy device, its equivalent inertia constant is related to the control mode and control parameters of the new energy device, and the equivalent inertia constant of the new energy device is calculated by the inertia identification method. The new energy device includes a new energy grid-connected converter, and the equivalent inertia constant of the new energy grid-connected converter calculated by the inertia identification method is used as the new energy grid-connected converter in the optimization model M1. The equivalent inertia constant The initial value of .

[0027] Furthermore, the minimum node inertia coefficient under the system frequency change rate constraint requirement is The calculation formula is:

[0028] in, is the number of devices in the power system; is the active power disturbance amplitude received by a single device, is the maximum frequency change rate required by the system.

[0029] Furthermore, the spatial distribution index of the system inertia The calculation formula is:

[0030] in, Owned by the system node The specific calculation formula is:

[0031] in, For Node The nodal inertia coefficient of is the number of nodes in the power system.

[0032] Furthermore, the system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia are all real numbers greater than 0.

[0033] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned node inertia-based power system inertia distribution optimization method when executing the computer program.

[0034] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned power system inertia distribution optimization method based on node inertia is implemented.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) The present invention aims to solve the problems that the power system inertia distribution optimization method based on the synchronous generator inertia center or frequency equivalent model cannot accurately evaluate the spatial distribution characteristics of the system inertia and the power system inertia distribution optimization method based on time domain simulation has low solution efficiency. The present invention considers the system frequency change rate constraint and the inertia spatial distribution constraint, and converts the system inertia constraint into an analytical expression, thereby realizing an efficient solution to the power system inertia distribution optimization, which can be used to improve the frequency stability of the power system.

[0036] (2) The present invention analyzes the inertia demand in the optimization of the inertia distribution of the power system, and can obtain the constraints related to the inertia demand in the optimization of the inertia distribution of the power system through the node inertia coefficient and the maximum frequency change rate required by the system. It can accurately evaluate the spatial distribution characteristics of the system inertia, making the characterization of the inertia demand of the power system more accurate.

[0037] (3) The spatial distribution constraint of the system inertia in the present invention is an analytical expression based on the node inertia coefficient. There is no need to judge whether the system inertia satisfies the frequency stability constraint through time domain simulation. The calculation time is greatly reduced, thereby meeting the requirements of real-time optimization of the system time-varying inertia. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flowchart of a method for optimizing the inertia distribution of a power system based on node inertia provided in an embodiment of the present invention.

[0039] Figure 2 It is an improved IEEE 39-node system network topology diagram provided by an embodiment of the present invention.

[0040] Figure 3 It is a bar chart showing the percentage change of node inertia coefficient before and after the optimization of power system inertia distribution. DETAILED DESCRIPTION

[0041] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0042] In some embodiments of the present invention, a specific example of a power system is provided for ease of understanding. The power system selects an improved IEEE 39-node system, and the system network topology is as follows: Figure 2 As shown in the figure, it includes: 39 nodes, numbered 1-39; 5 synchronous generators, numbered G1, G3, G6, G9 and G10; 5 new energy grid-connected converters, numbered G2, G4, G5, G7 and G8; on the basis of the standard IEEE 39-node system, the original synchronous generators G2, G4, G5, G7 and G8 are replaced by new energy grid-connected converters of the same capacity. The inertia constants of the synchronous generators are shown in Table 1, and the equivalent inertia constants of the new energy grid-connected converters are shown in Table 2.

[0043] Table 1 Inertia constants of synchronous generators

[0044] Table 2 Equivalent inertia constants of new energy grid-connected converters

[0045] See also Figure 1 The present invention provides a method for optimizing the inertia distribution of a power system based on node inertia, wherein the power system includes a plurality of nodes, and the method includes the following steps: Step 1: Set basic parameters, including system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia .

[0046] In this step, the system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia are all real numbers greater than 0.

[0047] In some embodiments of the present invention, let the system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system , the spatial distribution threshold of system inertia . System nominal frequency The value of is determined by the power system. The nominal frequency of my country's power system is , so in this embodiment, the system nominal frequency ; Regarding the maximum frequency change rate required by the system my country's power system does not make clear requirements on the maximum frequency change rate. It refers to the system frequency change rate protection limit of the South Australian power grid; the active power disturbance amplitude and the spatial distribution threshold of system inertia It is a value set manually according to system requirements.

[0048] Step 2: With the goal of minimizing the total system inertia cost, the equivalent inertia constants of all renewable energy grid-connected converters in the system are used as decision variables. The optimization model M1 is constructed by considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints, and the system inertia spatial distribution constraints.

[0049] The objective function expression in the optimization model M1 is as follows:

[0050] in, represents the total system inertia cost; A collection of new energy grid-connected converters; Grid-connected converter for new energy inertia cost.

[0051] Among them, new energy grid-connected converter Inertia cost The calculation formula is:

[0052] in, Grid-connected converter for new energy The equivalent inertia constant of , , and Grid-connected converter for new energy The inertia cost coefficient of the equivalent inertia constant is related to the control mode, current capacity and energy storage device of the equipment; Grid-connected converter for new energy The inertia cost threshold for the equivalent inertia constant.

[0053] In some embodiments of the present invention, the inertia cost coefficient and inertia cost threshold of the equivalent inertia constant of each new energy grid-connected converter are shown in Table 3.

[0054] Table 3 Inertia cost coefficient and threshold value of equivalent inertia constant of new energy grid-connected converter

[0055] The specific expressions of the constraints in the optimization model M1 are as follows: 1) Upper and lower limit constraints of equivalent inertia constant of new energy grid-connected converter:

[0056] in, Grid-connected converter for new energy The equivalent inertia constant of and Respectively, new energy grid-connected converter The minimum and maximum values ​​of the equivalent inertia constant are related to factors such as the control method, overcurrent capacity and energy storage device of the equipment.

[0057] In some embodiments of the present invention, the upper and lower limit constraints of the equivalent inertia constants of each renewable energy grid-connected converter are shown in Table 4.

[0058] Table 4 Control range of equivalent inertia constant of new energy grid-connected converter

[0059] 2) The expression of the system frequency change rate constraint is:

[0060] in, For Node The nodal inertia coefficient of The minimum value of the node inertia coefficient to meet the system frequency change rate constraint requirement; is the number of nodes in the power system; Among them, the node The node inertia coefficient The calculation formula is:

[0061] in, is the number of devices in the power system; For equipment in power systems Rated capacity; For equipment in power systems The equivalent inertia constant of Augmenting the admittance matrix for power systems No. Line Column elements.

[0062] Among them, equipment in the power system The equivalent inertia constant is a real number greater than or equal to 0. If the device is a synchronous generator, then its equivalent inertia constant is the inertia constant of the synchronous generator. The method for obtaining the inertia constant of the synchronous generator is known and will not be elaborated on here. If the device is a new energy device, its equivalent inertia constant is related to the control method and control parameters of the new energy device, and the value of its equivalent inertia constant before optimization can be calculated by the existing inertia identification method. The new energy device includes a new energy grid-connected converter, and the equivalent inertia constant of the new energy grid-connected converter calculated by the inertia identification method is used as the new energy grid-connected converter in the optimization model M1. The equivalent inertia constant In some embodiments of the present invention, the calculated equivalent inertia constant of the new energy grid-connected converter is the new energy grid-connected converter in the optimization model M1. The equivalent inertia constant The initial value of is shown in Table 2. In some embodiments of the present invention, the device includes 5 synchronous generators and 5 new energy grid-connected converters, and the number of devices is 10, that is, , the number of new energy grid-connected converters is 5, that is .

[0063] Augmented Admittance Matrix of Power System The calculation formula is:

[0064] in, is the imaginary part of the node admittance matrix; Indicates the size of the equivalent reactance inside the device. The diagonal elements of are the negative values ​​of the inverse of the internal equivalent reactance of the equipment, and the off-diagonal elements are 0; Indicates the connection relationship between the device and the power grid. Connect at the node ,So No. Line The elements of the column are devices The reciprocal of the internal equivalent reactance.

[0065] Minimum value of node inertia coefficient that meets the system frequency change rate constraint The calculation formula is:

[0066] The expression of the spatial distribution constraint of the system inertia is:

[0067] in, It is the spatial distribution index of system inertia.

[0068] System inertia spatial distribution index The calculation formula is:

[0069] in, is the average value of the node inertia coefficient of all nodes in the system. The specific calculation formula is:

[0070] Step 3: Use the optimization algorithm to solve the optimization model M1 and obtain the equivalent inertia constant set of all renewable energy grid-connected converters in the power system and total system inertia cost .

[0071] In some embodiments of the present invention, a genetic algorithm is used to solve the optimization model M1, and the equivalent inertia constant set of all new energy grid-connected converters in the power system after optimization is As shown in Table 5 for the equivalent inertia constant of the optimized model M1, the total system inertia cost is 232.

[0072] Step 4: With the goal of maximizing the overall inertia level of the system, the equivalent inertia constants of all renewable energy grid-connected converters in the system are taken as decision variables. The optimization model M2 is constructed by considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints, the system inertia spatial distribution constraints and the system inertia total cost constraints.

[0073] The objective function expression in the optimization model M2 is as follows:

[0074] in, Represents the overall inertia level of the system.

[0075] The optimization model M2 includes the following constraints: 1) Upper and lower limit constraints of equivalent inertia constant of new energy grid-connected converter:

[0076] 2) System frequency change rate constraint:

[0077] 3) System inertia spatial distribution constraints:

[0078] 4) System inertia total cost constraint:

[0079] Step 5: The equivalent inertia constants of all renewable energy grid-connected converters in the power system obtained by the optimization model M1 are set As the new energy grid-connected converter in the optimization model M2 The equivalent inertia constant The optimization algorithm is used to solve the optimization model M2 and output the equivalent inertia constants of all renewable energy grid-connected converters in the power system.

[0080] In some embodiments of the present invention, the optimization algorithm is a genetic algorithm.

[0081] The value of the equivalent inertia constant of all the equipment in the system determines the inertia distribution of the system. In some embodiments of the present invention, the equipment in the power system includes synchronous generators and new energy grid-connected converters, and the system inertia distribution is determined by the value of the equivalent inertia constant of all the synchronous generators and new energy grid-connected converters in the power system. Among them, the equivalent inertia constant of the synchronous generator is the inertia constant of the synchronous generator, and the value of the equivalent inertia constant of the new energy grid-connected converter is finally obtained by solving the optimization model M2.

[0082] In some embodiments of the present invention, the equivalent inertia constants of each new energy grid-connected converter after final optimization are shown in the equivalent inertia constants of the optimization model M2 in Table 5, and the total system inertia cost and overall inertia level before and after optimization are shown in Table 6. The percentage change of the node inertia coefficient before and after optimization is shown in Figure 3 shown.

[0083] Table 5 Optimization results of equivalent inertia constants of new energy grid-connected converters

[0084] Table 6 Total system inertia cost and overall inertia level before and after optimization

[0085] According to Table 6, after the optimization of the optimization model M1, the overall inertia level of the system is significantly improved; after the optimization of the optimization model M2, the overall inertia level of the system is further improved under the premise that the total inertia cost of the system remains unchanged. Figure 3 It can be seen that after optimization, the inertia of each node in the system has been improved, the maximum improvement of the node inertia coefficient is as high as 103.28%, and the average improvement is 46.78%, indicating that the overall inertia level of the system has been significantly improved. This shows that the optimization method proposed in the present invention can improve the inertia level of the power system, thereby improving the frequency stability of the power system.

[0086] In some embodiments of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the node inertia-based power system inertia distribution optimization method provided in the aforementioned embodiments is implemented.

[0087] In some embodiments of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the node inertia-based power system inertia distribution optimization method provided in the embodiment is implemented.

[0088] It should be understood by those skilled in the art that the present application embodiments can be provided as methods, equipment, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0089] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing inertia distribution of a power system based on node inertia, wherein the power system comprises a plurality of nodes, characterized in that: The following steps are involved: S1. Set basic parameters, including system nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia ; S2, with the goal of minimizing the total cost of system inertia, taking the equivalent inertia constants of all renewable energy grid-connected converters in the power system as decision variables, considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints and the system inertia spatial distribution constraints to build an optimization model M1; S3. Solve the optimization model M1 to obtain the equivalent inertia constant set of all renewable energy grid-connected converters in the power system and total system inertia cost ; S4, with the goal of maximizing the overall inertia level of the system, taking the equivalent inertia constants of all renewable energy grid-connected converters in the power system as decision variables, considering the upper and lower limit constraints of the equivalent inertia constants of renewable energy grid-connected converters, the system frequency change rate constraints, the system inertia spatial distribution constraints and the system inertia total cost constraints to construct the optimization model M2; S5. The equivalent inertia constants of all new energy grid-connected converters in the power system obtained by the optimization model M1 are set As the new energy grid-connected converter in the optimization model M2 The equivalent inertia constant The initial value of is used to solve the optimization model M2 and output the value of the equivalent inertia constant of the optimized new energy grid-connected converter.

2. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 1, characterized in that: The objective function expression in the optimization model M1 is as follows: in, represents the total system inertia cost; A collection of new energy grid-connected converters; Grid-connected converter for new energy Inertia cost; New Energy Grid-connected Converter Inertia cost The calculation formula is: in, , , and All are new energy grid-connected converters The inertia cost coefficient of the equivalent inertia constant; Grid-connected converter for new energy The inertia cost threshold for the equivalent inertia constant.

3. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 1, characterized in that: The objective function expression in the optimization model M2 is as follows: in, represents the overall inertia level of the system, For Node The nodal inertia coefficient of node The node inertia coefficient The calculation formula is: in, is the number of devices in the power system; For equipment in power systems Rated capacity; For equipment in power systems The equivalent inertia constant of Augmenting the admittance matrix for power systems No. Line Column elements.

4. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 3 is characterized in that: Augmented Admittance Matrix of Power System The calculation formula is: in, is the imaginary part of the node admittance matrix; Indicates the size of the equivalent reactance inside the device; Indicates the connection relationship between the device and the power grid.

5. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 3 is characterized in that: Equipment in power system The equivalent inertia constant is a real number greater than or equal to 0. If the device is a synchronous generator, the equivalent inertia constant of the synchronous generator is the inertia constant of the synchronous generator. If the device is a new energy device, the equivalent inertia constant of the new energy device is calculated by the inertia identification method. The new energy device includes a new energy grid-connected converter, and the equivalent inertia constant of the new energy grid-connected converter calculated by the inertia identification method is used as the new energy grid-connected converter in the optimization model M1. The equivalent inertia constant The initial value of .

6. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 1, characterized in that: The expression of the system frequency change rate constraint is: in, For Node The nodal inertia coefficient of The minimum value of the node inertia coefficient to meet the system frequency change rate constraint requirement; is the number of nodes in the power system; The minimum value of the node inertia coefficient that meets the system frequency change rate constraint The calculation formula is: in, is the number of devices in the power system; is the active power disturbance amplitude received by a single device, is the maximum frequency change rate required by the system.

7. The method for optimizing the inertia distribution of a power system based on node inertia according to claim 1, characterized in that: The expression of the spatial distribution constraint of the system inertia is: in, is the spatial distribution index of system inertia; System inertia spatial distribution index The calculation formula is: in, is the average value of the node inertia coefficient of all nodes in the power system. The specific calculation formula is: in, For Node The nodal inertia coefficient of is the number of nodes in the power system.

8. The method for optimizing the inertia distribution of a power system based on node inertia according to any one of claims 1 to 7, characterized in that: System nominal frequency , the active power disturbance amplitude of a single device , the maximum frequency change rate required by the system and the spatial distribution threshold of system inertia are all real numbers greater than 0.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for optimizing the inertia distribution of an electric power system based on node inertia according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for optimizing the inertia distribution of an electric power system based on node inertia as claimed in any one of claims 1 to 8 is implemented.

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