New energy frequency support control optimization method and system considering frequency space distribution

Abstract

The invention belongs to the technical field of new energy power control, and discloses a new energy frequency support control optimization method and system considering frequency space distribution, and the method comprises the steps: considering the frequency space distribution characteristics, constructing a multi-inertia center system frequency response model, carrying out the aggregation of parameters in the frequency response model through employing a region as a unit, and carrying out the optimization of the multi-inertia center system frequency response model. Obtaining a multi-area system frequency response model; analyzing the vibration process of the multi-region system frequency response model by using a modal analysis method to obtain a system frequency considering spatial distribution characteristics, and determining key indexes of the system frequency; establishing a relationship between the unit parameter and the system frequency key index, and determining the unit parameter according to the system frequency key index; and controlling the new energy power system to operate according to the unit parameters. According to the method, under the condition of considering the frequency space distribution, the system frequency characteristics are calculated through the modal analysis method, the relation between the unit parameters and the system frequency key indexes is established, and the control parameters can be optimized to improve the system frequency stability.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power control, and in particular to a new energy frequency support control optimization method and system considering frequency spatial distribution. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of new energy, the penetration rate of new energy units in the power system is gradually increasing, and the power system dominated by synchronous machines will gradually transform into a new energy power system. The traditional thermal power units in the system have certain parameters, while the parameters of the grid-type wind turbine units are adjustable. When selecting their parameters, the system frequency characteristics should be analyzed and appropriate parameters should be selected to meet the system frequency requirements.

[0004] Traditional power systems often ignore the differences in frequency spatial distribution. Existing methods for frequency analysis are generally based on the assumption that the frequencies of all nodes / busbars in the system are consistent, using the frequency of the center of inertia (COI) as the frequency of the entire network, ignoring the impact of the power grid, and weighting all generators in the system according to the size of inertia to be equivalent to an equivalent unit, which can well reflect the overall dynamic frequency changes of the system.

[0005] However, due to factors such as electrical distance, network geographical location, and differences in unit parameters, the frequency of the actual system has spatial distribution differences, and the frequency changes of each node are inconsistent. In a high-proportion renewable energy power system, the power supply is connected to the power grid through power electronic equipment, resulting in abnormally weak inertia of the renewable energy collection node, making the uneven spatial distribution of the power system frequency increasingly prominent and impossible to ignore. The traditional frequency analysis method based on COI is no longer applicable. Since it is difficult to describe the frequency space dynamic process of the renewable energy power system by considering only the COI frequency, the COI frequency is superimposed with the oscillation component to consider the spatial distribution characteristics of the system frequency.

[0006] The existence of distribution differences in actual system frequency space has attracted more and more attention. However, the current analysis of frequency space distribution is carried out under the condition that the unit parameters are determined, and does not consider how to determine the parameters of units with uncertain parameters (such as grid-type wind turbines). Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a new energy frequency support control optimization method and system considering the frequency spatial distribution. When considering the frequency spatial distribution, the system frequency characteristics are calculated by modal analysis, and the relationship between the unit parameters and the key indicators of the system frequency is established, so as to optimize the control parameters to improve the system frequency stability.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a new energy frequency support control optimization method considering frequency spatial distribution, comprising the following steps: Considering the frequency spatial distribution characteristics, a frequency response model of a multi-inertia center system is constructed, and the parameters in the frequency response model are aggregated in units of regions to obtain a multi-region system frequency response model. The modal analysis method is used to analyze the vibration process of the multi-region system frequency response model, obtain the system frequency considering the spatial distribution characteristics, and determine the key indicators of the system frequency; Establish the relationship between unit parameters and key system frequency indicators, and determine unit parameters based on key system frequency indicators; Control the operation of the renewable energy power system according to the unit parameters.

[0009] As an optional implementation, the frequency response model of the multi-inertia center system is:

[0010] Among them, Δ P m is the mechanical power change of the generator, Δ P e is the change in electromagnetic power of the generator, H is the generator inertia time constant, D is the load damping coefficient matrix.

[0011] As an optional implementation, the multi-region system frequency response model is:

[0012] As an optional implementation, the time domain expression of the system frequency considering the spatial distribution characteristics is:

[0013] As an optional implementation, the maximum frequency offset is selected as the key indicator of the system frequency.

[0014] As an optional implementation method, the relationship between the unit parameters and the key indicators of the system frequency is expressed as follows:

[0015] Among them, Δ f max is the maximum frequency deviation, H is the inertia time constant.

[0016] In a second aspect, the present invention provides a new energy frequency support control optimization system considering frequency spatial distribution, comprising: The model building module is configured to: consider the frequency spatial distribution characteristics, build a frequency response model of a multi-inertia center system, aggregate the parameters in the frequency response model by region, and obtain a multi-region system frequency response model; The model analysis module is configured to: analyze the vibration process of the multi-region system frequency response model using a modal analysis method, obtain the system frequency considering the spatial distribution characteristics, and determine the key indicators of the system frequency; The parameter determination module is configured to: establish a relationship between the unit parameters and the key indicators of the system frequency, and determine the unit parameters according to the key indicators of the system frequency; The control module is configured to control the operation of the new energy power system according to the unit parameters.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0019] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present disclosure proposes a new energy frequency support control optimization method and system considering the frequency spatial distribution. In the case of considering the frequency spatial distribution, a multi-inertia center system frequency response model is established, and the system frequency characteristics are calculated by modal analysis to determine the system frequency key indicators. The relationship between the unit parameters and the system frequency key indicators is established, so that the parameters to be selected by the unit can be effectively determined according to the requirements for the system frequency key indicators. The present disclosure can optimize the control parameters to improve the system frequency stability.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 It is a flow chart of the new energy frequency support control optimization method considering frequency spatial distribution of the present invention; Figure 2 A transfer function block diagram in matrix form of a system frequency response model considering spatial distribution characteristics of the present invention; Figure 3 A transfer function block diagram of a frequency response model of a certain area of ​​the present invention; Figure 4 is a simplified system frequency response model of the present invention; Figure 5 This is the structural diagram of the three-machine nine-node system; Figure 6 This is a comparison chart of the frequency calculation results of a three-machine nine-node system; Figure 7 This is the relationship diagram between the second parameter of the unit and the system frequency; Figure 8 This is the fitting result diagram of the relationship between the second parameter of the unit and the system frequency; Fig. 9 The system frequency is when H=5.33; Fig.10 This is the system frequency when H=4. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0028] Example 1 like Figure 1As shown, this embodiment provides a new energy frequency support control optimization method considering frequency spatial distribution, including the following steps: S1 considers the frequency spatial distribution characteristics, constructs a frequency response model of a multi-inertia center system, aggregates the parameters in the frequency response model by region, and obtains a multi-region system frequency response model; S2 uses modal analysis to analyze the vibration process of the multi-region system frequency response model, obtains the system frequency considering the spatial distribution characteristics, and determines the key indicators of the system frequency; S3 establishes the relationship between unit parameters and key indicators of system frequency, and determines unit parameters according to the key indicators of system frequency; S4 controls the operation of the renewable energy power system according to the unit parameters.

[0029] First, considering the frequency spatial distribution characteristics, the rotor motion equation of the multi-inertia center system (i.e., the frequency response model of the multi-inertia center system) is established as follows: (1) Among them, Δ P m is the mechanical power change of the generator, Δ P e is the change in electromagnetic power of the generator, H is the generator inertia time constant, D is the load damping coefficient matrix, Δω is the system frequency change.

[0030] The transfer function block diagram in matrix form is as follows Figure 2 As shown, the power oscillation component between multiple machines is considered.

[0031] The power system network equation based on AC power flow can express the connection relationship between multi-machine systems. X Generally much larger than the resistance R , the resistance can usually be ignored, and it is assumed that the voltage phase angle difference between the two ends of the line is very small, that is, sin( θ i - θ j ) ≈ θ i - θ j Therefore, when calculating the active power flow under the initial working condition, the AC network model can be simplified to a DC power flow network model: (2) In the formula, Δ P L is the active disturbance of the load node, Δ P e is the electromagnetic power of the generator node, ∆ θ and ∆ θ L is the generator node power angle and load node phase angle, B GG for n Generator d Shaft sub-transient reactance The diagonal matrix is ​​composed of B GG =diag[ , ,…, ], B LL To keep only the imaginary part m dimensional node admittance matrix, B GL Represents the connection relationship and electrical distance between the generator node and the busbar node. From formula (2), the electromagnetic power Δ P e The expression is: (3) That is, the electromagnetic power of the generator Δ P e is the disturbance power allocation Δ P d and the inter-generator power oscillation component Δ P OSC The superposition of B l is the distribution coefficient matrix of the system disturbance power to each generator rotor, the matrix B s represents the interaction between the rotors of each generator, and the calculation formulas are: (4) The mechanical power expression in matrix form is: (5) In the formula, K m , R , F H and T R They are all the primary frequency modulation parameter matrices of the system in the SFR model. I is a unit matrix.

[0032] According to the partition results of the power system, the parameters in the frequency response model are aggregated in units of regions to obtain a multi-region system frequency response model. Here we record: (6) Based on the existing multi-machine system parameter aggregation method, regional aggregation parameters The calculation formula is: (7) In the formula, S Ni For generator i The capacity, S B The base capacity.

[0033] Then, by substituting equation (3) into equation (1) and aggregating them, we can get the frequency response model of the multi-region system: (8) in, are the regional aggregation parameters for aggregation according to formula (6) and (7), and before aggregation they are .

[0034] Taking a certain area as an example, the transfer function diagram of the regional frequency response model is as follows: Figure 3 shown.

[0035] The calculation formula for the active disturbance and power oscillation components in this area is: (9) in, is the disturbance power allocation of node 1, is the inter-generator power oscillation component of node 1, ... ... is the distribution coefficient of the system disturbance power to node 1, ... ... Represents the interaction between each generator rotor and node 1.

[0036] The modal analysis method can be used to analyze the vibration process. First, write equation (8) in the form of a forced vibration equation as follows: (10) Then, the mechanical power is modulated once. This breaks down into: (11) In the formula, Small, can be ignored, It can be equivalent to the damping coefficient. The system frequency response model considering the spatial distribution characteristics is as follows: Figure 4 shown.

[0037] At this time, the vibration equation of formula (11) can be simplified to (12) In the formula, the equivalent damping for (13) The system vibration dynamics equation without external force corresponding to formula (12) is: (14) This equation has a special solution (15) Solved 2 n Eigenvalues λ i ( i =1,2,…,2 n ) is a conjugate complex number, and they are arranged from small to large as (16) Since the node admittance matrix The sum of each row of is 0 (i.e., insufficient rank), and 0 is always an eigenvalue of the characteristic equation λ 1. Its λ 2 corresponds to the dynamic process of the system COI frequency, at which time there is no relative motion between the regions. n The eigenvector corresponding to the conjugate complex eigenvalue Φ (i) ( i =1,2,…,2 n ) is also a conjugate complex number, that is, a complex vibration type, and we have: (17) The complex vibration type Φ (i) Listed as n ×2 n Order Matrix Φ , and the complex eigenvalues λ i Diagonal Matrix Λ : (18) Then, in order to convert the damped system equation (14) into a first-order equation system with 2n variables, decoupling transformation is realized and the variable is added ,make, (19) At the same time, add the identity, (20) Then the kinetic equation (14) can be transformed into 2 n The state equation is: (twenty one) In the formula, the matrix and They are all diagonal matrices, and the expressions are: (twenty two) Then, solve the eigenvalue of the vibration equation (21) Ψ and the eigenvector λ , we can get the variable y The complex mode Ψ With variables The complex mode Φ Satisfy between: (twenty three) Rule 2 n Complex vibration type Ψ (i) ( i =1,2,…,2 n ) can be listed as 2 n ×2 n Complex Vibration Matrix Ψ According to the relevant knowledge of vibration mechanics, 2 n Complex vibration type Ψ (i) Orthogonality is obtained. The orthogonality condition can be derived: (twenty four) Then we can get: (25) In the formula, and Denote the diagonal inertia matrix and the diagonal node admittance matrix. Let i = j , we can get the relationship between the two diagonal matrices: (26) 2 n feature vector Ψ (i) The orthogonality of indicates that they are linearly independent, so they can be used as the basis of the 2n-dimensional space (principal coordinate system). Any vibration of the generator rotor in the system can be uniquely expressed as a linear combination of vibrations of various orders. y Make variable substitution, let, (27) Substituting it into equation (21), using orthogonality, we can get z =( z j ) is the state equation of the variable: (28) Then, using formula (26), it can be simplified to 2 n A decoupled linear differential equation of the first order: (29) Then the general solution of equation (29) is: (30) (31) In the formula, C is a constant, assuming that the initial conditions of the original system (12) are: (32) Convert it to the principal coordinate system: (33) Then, substituting it into equation (30), we can get the solution of equation (29) as (34) (35) Then, according to formula (19), y The definition of , and the coordinate transformation using equations (23) and (27) can give the time domain solution of the original system: (36) (37) However, since the primary frequency modulation response process is ignored in the simplified calculation, the COI frequency curve obtained by the first pair of conjugate negative eigenvalues ​​and eigenvectors cannot reflect the actual quasi-steady-state stage. Therefore, the traditional SFR model is used to calculate the system COI frequency.

[0038] (38) Others in formula (37) n -1 pairs of conjugate complex eigenvalues ​​and eigenvectors characterize the mutual motion between the generator rotors, corresponding to the system frequency oscillation components.

[0039] (39) In summary, the time domain expression of the system frequency considering the spatial distribution characteristics can be obtained as follows: (40) Among them, the parameters in the formula are the parameters involved in calculating the COI frequency of the system using the traditional SFR model, and the calculation method is as shown in formula (41): (41) The main parameters of the unit are inertia time constant H , select the maximum frequency offset Δ f max The main parameters of the system frequency are used to establish the relationship between the main parameters and the system frequency index. i UnitG i , changing its inertia constant H i , the generator node frequency is calculated using the modal analysis method. The node frequency expression is formula (40), and the first i Generator sets G i In different parameters H i The maximum frequency deviation Δ f max .

[0040] For different frequencies, the maximum deviation Δ f max and H The data is fitted and the relationship between the two is obtained as formula (42).

[0041] (42) Given the maximum frequency deviation Δ required by the system f max When , we can use formula (42) H Maximum deviation from frequency Δ f max Determine the corresponding unit H The value of .

[0042] The present disclosure is of great significance for determining the parameters of the unit. In the case of considering the frequency spatial distribution, the system frequency characteristics are calculated by modal analysis. The relationship between the unit parameters and the key indicators of the system frequency is established, so that the parameters to be selected for the unit can be effectively determined according to the requirements for the key indicators of the system frequency. The use of the present invention will effectively improve the stability of the system frequency.

[0043] The technical solution of the present disclosure is introduced below with a specific example: Establish a three-machine nine-node model such as Figure 5 , select the parameters of each generator according to Table 1.

[0044] Table 1 Generator parameters of three-machine nine-bus system

[0045] Set up an active disturbance experiment for a three-machine nine-bus system. t =0s, an active disturbance of 0.12pu occurs at node 6 in the system, and the frequency of the system generator node is calculated using the above modal analysis method.

[0046] The actual frequency curve of the system is as follows: Figure 6, where the solid line is the time domain simulation result and the dotted line is the analytical calculation result. Compared with the time domain simulation model, the analytical calculation results can well reflect the frequency oscillation process of each generator. The lowest frequency point obtained by the time domain simulation model is -0.2205Hz, and the calculated value of the modal analysis method is -0.2211Hz, with a relative error of only 0.26%.

[0047] Change the inertia constant of unit 2 H , the generator node frequency is calculated by modal analysis method, and the relationship between the generator set parameters and the maximum frequency deviation is obtained as follows: Figure 7 .

[0048] exist H When it is larger, the maximum frequency deviation changes more regularly, which is similar to H There is an obvious positive correlation. H >0.3 part of the data, fitting H Relationship with the maximum deviation of system frequency.

[0049] The fitting results are as follows Figure 8 As shown, the relational expression is: (42) When the maximum frequency deviation requirement is known, the H Determine the relationship with the maximum frequency deviation H value.

[0050] When the maximum system frequency deviation is required to be Δf max =-0.2440, the maximum deviation of the passing frequency is H relationship, get H =5.33, the second unit H Set it to 5.33 and perform simulation verification. The results are as follows Fig. 9 shown.

[0051] At this time, the maximum frequency deviation Δ f max =-0.2439, which meets the requirement of maximum frequency deviation.

[0052] If the technical solution of the present disclosure is not adopted, when the maximum system frequency deviation is required to be Δ f max =-0.2440, according to Figure 7 It can be seen that H >0 can meet the requirements. However, in the actual system considering the frequency space distribution, H <5.33, the actual system frequency maximum deviation exceeds Δ f max =-0.2440, if we take H=4, the maximum system frequency deviation reaches Δ f max =-0.2448, e.g. Fig.10 , does not meet the requirements.

[0053] Example 2 This embodiment provides a new energy frequency support control optimization system considering frequency spatial distribution, including: The model building module is configured to: consider the frequency spatial distribution characteristics, build a frequency response model of a multi-inertia center system, aggregate the parameters in the frequency response model by region, and obtain a multi-region system frequency response model; The model analysis module is configured to: analyze the vibration process of the multi-region system frequency response model using a modal analysis method, obtain the system frequency considering the spatial distribution characteristics, and determine the key indicators of the system frequency; The parameter determination module is configured to: establish a relationship between the unit parameters and the key indicators of the system frequency, and determine the unit parameters according to the key indicators of the system frequency; The control module is configured to control the operation of the new energy power system according to the unit parameters.

[0054] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0055] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0056] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0057] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0058] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0059] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0060] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0061] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0062] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0063] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, and the like.

[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A new energy frequency support control optimization method considering frequency spatial distribution, characterized in that: The following steps are involved: Considering the frequency spatial distribution characteristics, a frequency response model of a multi-inertia center system is constructed, and the parameters in the frequency response model are aggregated in units of regions to obtain a multi-region system frequency response model. The modal analysis method is used to analyze the vibration process of the multi-region system frequency response model, obtain the system frequency considering the spatial distribution characteristics, and determine the key indicators of the system frequency; Establish the relationship between unit parameters and key system frequency indicators, and determine unit parameters based on key system frequency indicators; Control the operation of the renewable energy power system according to the unit parameters.

2. The new energy frequency support control optimization method considering frequency spatial distribution as claimed in claim 1, characterized in that: The frequency response model of the multi-inertia center system is: Among them, Δ P m is the mechanical power change of the generator, Δ P e is the change in electromagnetic power of the generator, H is the generator inertia time constant, D is the load damping coefficient matrix.

3. The new energy frequency support control optimization method considering frequency spatial distribution as claimed in claim 1, characterized in that: The multi-region system frequency response model is: 。 4. The new energy frequency support control optimization method considering frequency spatial distribution as claimed in claim 1, characterized in that: The time domain expression of the system frequency considering the spatial distribution characteristics is: 。 5. The new energy frequency support control optimization method considering frequency spatial distribution as claimed in claim 1, characterized in that: The maximum frequency offset is selected as the key indicator of system frequency.

6. The new energy frequency support control optimization method considering frequency spatial distribution as claimed in claim 1, characterized in that: The relationship between the unit parameters and the key indicators of system frequency is expressed as: Among them, Δ f max is the maximum frequency deviation, H is the inertia time constant.

7. A new energy frequency support control optimization system considering frequency spatial distribution, characterized in that: include: The model building module is configured to: consider the frequency spatial distribution characteristics, build a frequency response model of a multi-inertia center system, aggregate the parameters in the frequency response model by region, and obtain a multi-region system frequency response model; The model analysis module is configured to: analyze the vibration process of the multi-region system frequency response model using a modal analysis method, obtain the system frequency considering the spatial distribution characteristics, and determine the key indicators of the system frequency; The parameter determination module is configured to: establish a relationship between the unit parameters and the key indicators of the system frequency, and determine the unit parameters according to the key indicators of the system frequency; The control module is configured to control the operation of the new energy power system according to the unit parameters.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images