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

Abstract

The present invention discloses a method, device and medium for optimizing the inertia distribution of an electric power system based on node inertia. The method comprises: setting basic parameters; taking minimizing the total cost of system inertia as the goal, considering the upper and lower limit constraints of the equivalent inertia constant of the new energy grid-connected converter, the system frequency change rate constraint and the system inertia space distribution constraint to construct an optimization model M1; solving the optimization model M1 to obtain the total cost of system inertia; taking maximizing the overall inertia level of the system as the goal, considering the upper and lower limit constraints of the equivalent inertia constant of the new energy grid-connected converter, the system frequency change rate constraint, the system inertia space distribution constraint and the system inertia total cost constraint to construct an optimization model M2; solving the optimization model M2, and outputting the value of the equivalent inertia constant of the new energy grid-connected converter. The present invention takes into account the frequency change rate constraint and the inertia space distribution constraint, realizes the efficient solution of the optimization of the inertia distribution of the electric power system, and can be used to improve the frequency stability of the electric power system.

Description

Technical Field

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

[0002] After a power system disturbance, a large frequency change rate can cause damage to synchronous generators, disconnection of new energy equipment from the grid, and even system frequency collapse, impacting system frequency stability. System inertia plays a key role in the early stages of a disturbance. Insufficient inertia can directly lead to an excessive frequency change rate. Therefore, evaluating and optimizing system inertia is crucial for ensuring frequency stability.

[0003] In systems with a high proportion of renewable energy, differences in converter inertia control methods (such as virtual inertia control and virtual synchronous generator control) and their parameters lead to uneven system inertia distribution. During power disturbances, the greater the difference in system inertia, the more severe the frequency oscillation. Therefore, significant spatial differences in system inertia should be avoided. Furthermore, the intermittent and fluctuating nature of renewable energy generation leads to frequent changes in equipment status, resulting in significant time-varying system inertia. Fast and accurate inertia distribution optimization methods are urgently needed to meet real-time requirements.

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

[0005] Most existing technologies derive the inertia requirements for power system inertia distribution optimization from models equivalent to the synchronous generator's center of inertia or frequency. This fails to accurately assess the spatial distribution characteristics of the system's inertia, significantly reducing the accuracy of the results. Some technologies utilize time-domain simulation to analyze inertia requirements for power system inertia distribution optimization. However, as system scale expands and the proportion of renewable energy increases, the system model dimension increases dramatically, the required computational step size decreases, and the time-domain simulation speed decreases dramatically. Consequently, time-domain simulation faces a conflict between accuracy and speed, making it difficult to meet the requirements for 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 multiple nodes, and the optimization method includes the following steps:

[0008] 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 .

[0009] 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 used 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, an optimization model M1 is constructed.

[0010] The objective function expression in the optimization model M1 is as follows:

[0011]

[0012] in, represents the total cost of system inertia; A collection of new energy grid-connected converters; New energy grid-connected converter inertia cost.

[0013] 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 .

[0014] 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 used 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.

[0015] The objective function expression in the optimization model M2 is as follows:

[0016]

[0017] in, Indicates the overall inertia level of the system.

[0018] Among them, the constraint expressions in the optimization model M1 and the optimization model M2 are:

[0019] 1) The upper and lower limit constraints of the equivalent inertia constant of the new energy grid-connected converter are expressed as follows:

[0020]

[0021] in, New energy grid-connected converter The equivalent inertia constant of and New energy grid-connected converters The minimum and maximum values ​​of the equivalent inertia constant are related to factors such as the equipment's control mode, overcurrent capacity, and energy storage device;

[0022] 2) The expression of the system frequency change rate constraint is:

[0023]

[0024] in, For nodes The nodal inertia coefficient of To meet the minimum value of the node inertia coefficient under the system frequency change rate constraint requirement; is the number of nodes in the power system;

[0025] 3) The expression of the spatial distribution constraint of the system inertia is:

[0026]

[0027] in, is the spatial distribution index of the system inertia;

[0028] 4) The expression of the total cost constraint of system inertia is:

[0029]

[0030] S5. 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 value of the equivalent inertia constant of the optimized new energy grid-connected converter.

[0031] Furthermore, new energy grid-connected converters Inertia cost The calculation formula is:

[0032]

[0033] in, 、 、 and All are new energy grid-connected converters The inertia cost coefficient of the equivalent inertia constant; New energy grid-connected converter The inertia cost threshold of the equivalent inertia constant.

[0034] Furthermore, the node Node inertia coefficient The calculation formula is:

[0035]

[0036] 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. Rank Column element.

[0037] Furthermore, the system augmented admittance matrix The calculation formula is:

[0038]

[0039] 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. If the device Connect at the node ,So No. Rank The elements of the column are devices The reciprocal of the internal equivalent reactance.

[0040] 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 .

[0041] Furthermore, the minimum value of the node inertia coefficient under the system frequency change rate constraint is satisfied. The calculation formula is:

[0042]

[0043] 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.

[0044] Furthermore, the spatial distribution index of the system inertia The calculation formula is:

[0045]

[0046] in, Owned by the system node The specific calculation formula is:

[0047]

[0048] in, For nodes The nodal inertia coefficient of is the number of nodes in the power system.

[0049] 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.

[0050] 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.

[0051] 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 power system inertia distribution optimization method based on node inertia is implemented.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0053] (1) The present invention addresses 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 achieving an efficient solution to the power system inertia distribution optimization, which can be used to improve the frequency stability of the power system.

[0054] (2) The present invention analyzes the inertia demand in the optimization of the inertia distribution of the power system. The constraints related to the inertia demand in the optimization of the inertia distribution of the power system can be obtained through the node inertia coefficient and the maximum frequency change rate required by the system. The spatial distribution characteristics of the system inertia can be accurately evaluated, making the characterization of the inertia demand of the power system more accurate.

[0055] (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 use time domain simulation to determine whether the system inertia meets the frequency stability constraint. 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

[0056] Figure 1 This is a flowchart of a method for optimizing power system inertia distribution based on node inertia provided in an embodiment of the present invention.

[0057] Figure 2 This is a network topology diagram of an improved IEEE 39-node system provided by an embodiment of the present invention.

[0058] Figure 3 It is a bar chart showing the percentage change of node inertia coefficient before and after optimization of power system inertia distribution. DETAILED DESCRIPTION

[0059] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0060] 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 2As shown in the figure, it includes: 39 nodes, numbered 1-39; five synchronous generators, numbered G1, G3, G6, G9, and G10; and five renewable energy grid-connected converters, numbered G2, G4, G5, G7, and G8. Based on the standard IEEE 39-node system, the original synchronous generators G2, G4, G5, G7, and G8 are replaced with renewable 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 renewable energy grid-connected converters are shown in Table 2.

[0061] Table 1 Inertia constants of synchronous generators

[0062]

[0063] Table 2 Equivalent inertia constants of new energy grid-connected converters

[0064]

[0065] 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:

[0066] 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 .

[0067] 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.

[0068] 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 is ; About 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. Refers to the system frequency change rate protection limit of the South Australian power grid; active power disturbance amplitude and the spatial distribution threshold of system inertia It is a value set manually according to system requirements.

[0069] 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. 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, an optimization model M1 is constructed.

[0070] The objective function expression in the optimization model M1 is as follows:

[0071]

[0072] in, represents the total cost of system inertia; A collection of new energy grid-connected converters; New energy grid-connected converter inertia cost.

[0073] Among them, new energy grid-connected converter Inertia cost The calculation formula is:

[0074]

[0075] in, New energy grid-connected converter The equivalent inertia constant of 、 、 and New energy grid-connected converter The inertia cost coefficient of the equivalent inertia constant is related to the control mode, overcurrent capacity and energy storage device of the equipment; New energy grid-connected converter The inertia cost threshold of the equivalent inertia constant.

[0076] 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.

[0077] Table 3 Inertia cost coefficient and threshold value of equivalent inertia constant of new energy grid-connected converter

[0078]

[0079] The specific expressions of the constraints in the optimization model M1 are as follows:

[0080] 1) Upper and lower limit constraints of equivalent inertia constant of new energy grid-connected converter:

[0081]

[0082] in, New energy grid-connected converter The equivalent inertia constant of and New energy grid-connected converters 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.

[0083] 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.

[0084] Table 4 Control range of equivalent inertia constant of new energy grid-connected converter

[0085]

[0086] 2) The expression of the system frequency change rate constraint is:

[0087]

[0088] in, For nodes The nodal inertia coefficient of The minimum value of the node inertia coefficient that meets the system frequency change rate constraint requirement; is the number of nodes in the power system;

[0089] Among them, the node Node inertia coefficient The calculation formula is:

[0090]

[0091] 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. Rank Column element.

[0092] 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 .

[0093] Power system augmented admittance matrix The calculation formula is:

[0094]

[0095] 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. If the device Connect at the node ,So No. Rank The elements of the column are devices The reciprocal of the internal equivalent reactance.

[0096] Minimum value of node inertia coefficient that meets the system frequency change rate constraint The calculation formula is:

[0097]

[0098] The expression of the spatial distribution constraint of the system inertia is:

[0099]

[0100] in, It is the spatial distribution index of the system inertia.

[0101] System inertia spatial distribution index The calculation formula is:

[0102]

[0103] in, is the average value of the node inertia coefficient of all nodes in the system. The specific calculation formula is:

[0104]

[0105] 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 .

[0106] 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 renewable energy grid-connected converters in the power system after optimization is As shown in Table 5, the equivalent inertia constant of the optimized model M1 is is 232.

[0107] 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 used 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 constraint, the system inertia spatial distribution constraint, and the system inertia total cost constraint, an optimization model M2 is constructed.

[0108] The objective function expression in the optimization model M2 is as follows:

[0109]

[0110] in, Indicates the overall inertia level of the system.

[0111] The optimization model M2 includes the following constraints:

[0112] 1) Upper and lower limit constraints of equivalent inertia constant of new energy grid-connected converter:

[0113]

[0114] 2) System frequency change rate constraint:

[0115]

[0116] 3) System inertia spatial distribution constraints:

[0117]

[0118] 4) System inertia total cost constraint:

[0119]

[0120] 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 constant values ​​of all renewable energy grid-connected converters in the power system.

[0121] In some embodiments of the present invention, the optimization algorithm is a genetic algorithm.

[0122] The values ​​of the equivalent inertia constants of all equipment in the system determine the system inertia distribution. In some embodiments of the present invention, the equipment in the power system includes synchronous generators and renewable energy grid-connected converters. The system inertia distribution is determined by the values ​​of the equivalent inertia constants of all synchronous generators and renewable energy grid-connected converters in the power system. The equivalent inertia constant of the synchronous generator is the inertia constant of the synchronous generator, while the value of the equivalent inertia constant of the renewable energy grid-connected converter is ultimately determined by solving the optimization model M2.

[0123] In some embodiments of the present invention, the equivalent inertia constants of each renewable energy grid-connected converter after optimization are shown in Table 5 for the equivalent inertia constants of the optimization model M2, 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 Table 6. Figure 3 shown.

[0124] Table 5 Optimization results of equivalent inertia constants of new energy grid-connected converters

[0125]

[0126] Table 6 Total system inertia cost and overall inertia level before and after optimization

[0127]

[0128] 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 cost of the system inertia remains unchanged. Figure 3As can be seen, after optimization, the inertia of each node in the system has increased, with the maximum increase in node inertia coefficient reaching 103.28% and the average increase being 46.78%, indicating a significant improvement in the overall system inertia. This demonstrates that the optimization method proposed in this paper can improve the inertia level of the power system, thereby improving the frequency stability of the power system.

[0129] 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.

[0130] In some embodiments of the present invention, a computer-readable storage medium is further 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.

[0131] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0132] 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 produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0133] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1The steps of a specified function in a process or multiple processes.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the 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 by 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 and is 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 system inertia cost, the equivalent inertia constants of all renewable energy grid-connected converters in the power system are used as decision variables. An 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 constraint, and the system inertia spatial distribution constraint. 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 system inertia level, the equivalent inertia constants of all renewable energy grid-connected converters in the power system are used as decision variables. An 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 constraint, the system inertia spatial distribution constraint, and the system inertia total cost constraint. The expression of the spatial distribution constraint of the system inertia is: in, is the spatial distribution index of the 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 nodes The nodal inertia coefficient; is the number of nodes in the power system; node The nodal 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. Rank Column elements; S5. 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 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 power system inertia distribution optimization method based on node inertia according to claim 1 is characterized in that: The objective function expression in the optimization model M1 is as follows: in, represents the total cost of system inertia; A collection of new energy grid-connected converters; New energy grid-connected converter 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; New energy grid-connected converter The inertia cost threshold of the equivalent inertia constant.

3. The power system inertia distribution optimization method based on node inertia according to claim 1, characterized in that: The objective function expression in the optimization model M2 is as follows: in, Indicates the overall inertia level of the system, For nodes The inertia coefficient of the node.

4. The power system inertia distribution optimization method based on node inertia according to claim 3 is characterized in that: Power system augmented admittance matrix 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 power system inertia distribution optimization method based on node inertia according to claim 3 is characterized in that: Equipment in power systems The equivalent inertia constant is a real number greater than 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 power system inertia distribution optimization method based on node inertia according to claim 3 is characterized in that: The expression of the system frequency change rate constraint is: in, For nodes The nodal inertia coefficient; The minimum value of the node inertia coefficient that meets 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 power system inertia distribution based on node inertia according to any one of claims 1 to 6, 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.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for optimizing the inertia distribution of a power system based on node inertia according to any one of claims 1 to 7 is implemented.

9. 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 a power system based on node inertia according to any one of claims 1 to 7 is implemented.

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