Partition configuration method, device and equipment for virtual inertia of new energy power system

Abstract

The invention relates to the technical field of electric power, in particular to a partition configuration method, device and equipment for virtual inertia of a new energy electric power system. The partition configuration method for the virtual inertia of the new energy power system comprises the following steps: partitioning the new energy power system according to an electrical distance between each generator node in the new energy power system and an incidence matrix between a network node and each generator node in the new energy power system to obtain a plurality of first partitions; determining a multi-region frequency response model of the new energy power system; and constructing a partition configuration model of the virtual inertia of the new energy power system in the multi-disturbance scene based on the target function and the constraint condition, and configuring the virtual inertia for the photovoltaic unit and the wind turbine unit based on the partition configuration model of the virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a partition configuration method, device and equipment of virtual inertia of a new energy power system. BACKGROUND

[0002] With the transformation of global energy structure to environmental protection, a large number of new energy units (such as wind power units and photovoltaic units) gradually replace traditional synchronous machines to access the power system. Compared with traditional units, the inertia support capability of new energy units is weak and presents nonlinear characteristics, which leads to the challenge of frequency stability of the power system under disturbance. To address this problem, by dynamically adjusting the virtual inertia control coefficient of new energy units, the active frequency support capability is enhanced, which becomes a key means to improve the anti-disturbance performance of the system. However, the scale of interconnected system containing new energy continues to expand, and the probability of fault disturbance in each region is different due to the difference in environment and operating conditions. The inertia configuration scheme under a single disturbance scenario is difficult to adapt to complex and variable actual working conditions. Excessive increase of inertia may lead to economic decline, and insufficient inertia may affect the stability of the system.

[0003] In some scenarios, for wind power units, the optimal configuration of virtual inertia control coefficient is solved by analyzing the inertia demand under frequency safety constraint, using improved optimization algorithm or sensitivity analysis method, to improve the active support capability; for photovoltaic units, the variable virtual inertia control strategy is designed or combined with root locus method to optimize the virtual inertia and damping coefficient, to realize the frequency fluctuation suppression. However, these methods focus on the independent configuration of single type unit, and do not fully consider the coordination between wind power units and photovoltaic units, so it is difficult to ensure the anti-disturbance performance and stability of the power system. Therefore, the active frequency control parameters of wind power units and photovoltaic units in the actual power system are controllable, how to configure appropriate virtual inertia control coefficient, considering the economy while ensuring the coordinated operation of different types of units to ensure the anti-disturbance performance and stability of the power system is a technical problem to be solved by those skilled in the art. SUMMARY

[0004] In order to solve the technical problem of low anti-disturbance performance and stability of the power system, the purpose of the present application is to provide a partition configuration method, device and equipment of virtual inertia of a new energy power system, and the technical scheme adopted is as follows:

[0005] In a first aspect, the embodiments of the present application disclose a method for partitioning configuration of virtual inertia of a new energy power system, comprising: partitioning the new energy power system according to electrical distances between generator nodes in the new energy power system and an association matrix between network nodes and the generator nodes in the new energy power system, to obtain a plurality of first partitions, the generator nodes including photovoltaic units and wind power units; determining a multi-region frequency response model of the new energy power system according to parameters of the photovoltaic units and the wind power units in each first partition and equivalent tie-line parameters between the first partitions; taking minimum cost of the new energy power system as a target, constructing an objective function of virtual inertia configuration of the photovoltaic units and the wind power units in a multi-disturbance scenario, taking maximum frequency deviation and frequency change rate as quantitative indexes, and determining constraint conditions of the virtual inertia configuration of the photovoltaic units and the wind power units in the multi-disturbance scenario; constructing a partitioning configuration model of virtual inertia of the new energy power system in the multi-disturbance scenario based on the objective function and the constraint conditions, and configuring virtual inertia to the photovoltaic units and the wind power units based on the partitioning configuration model of virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system.

[0006] In a second aspect, the embodiments of the present application provide a device for partitioning configuration of virtual inertia of a new energy power system, comprising: a partitioning module configured to partition the new energy power system according to electrical distances between generator nodes in the new energy power system and an association matrix between network nodes and the generator nodes in the new energy power system, to obtain a plurality of first partitions, the generator nodes including photovoltaic units and wind power units; a determining module configured to determine a multi-region frequency response model of the new energy power system according to parameters of the photovoltaic units and the wind power units in each first partition and equivalent tie-line parameters between the first partitions; the determining module is further configured to take minimum cost of the new energy power system as a target, construct an objective function of virtual inertia configuration of the photovoltaic units and the wind power units in a multi-disturbance scenario, take maximum frequency deviation and frequency change rate as quantitative indexes, and determine constraint conditions of the virtual inertia configuration of the photovoltaic units and the wind power units in the multi-disturbance scenario; and a configuration module configured to construct a partitioning configuration model of virtual inertia of the new energy power system in the multi-disturbance scenario based on the objective function and the constraint conditions, and configure virtual inertia to the photovoltaic units and the wind power units based on the partitioning configuration model of virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system.

[0007] In a third aspect, the embodiments of the present application provide an electronic device, comprising: a processor and a memory; wherein the memory is configured to store a computer program capable of running on the processor; and the processor is configured to execute the program stored on the memory, to implement the steps of the method for partitioning configuration of virtual inertia of a new energy power system as mentioned in the first aspect.

[0008] The application has the following beneficial effects: in view of the difference of disturbance probability of different partitions, the virtual inertia configuration is carried out in a multi-disturbance scene, so that the configuration result can adapt to complex and changeable actual working conditions. Through the control of the frequency quantization index in the constraint condition, it is ensured that the photovoltaic unit and the wind turbine can provide effective inertia support under various disturbances, suppress frequency fluctuation and reduce the risk of local frequency out-of-limit. Compared with the configuration scheme of a single disturbance scene, the overall anti-disturbance ability of the new energy power system can be significantly improved, the limitation of traditional independent configuration of single type unit is broken, the coordination relationship of virtual inertia of the photovoltaic unit and the wind turbine is considered as a whole through the partition configuration model, the complementary advantages of different types of new energy units are realized by combining the inertia response characteristics of the two, the overall inertia resource of the power system is optimally utilized, and the anti-disturbance performance, stability and economic effectiveness of the power system are improved.

[0009] In addition, the embodiment of the application partitions by the electrical distance between the generator nodes and the association matrix of the network nodes, so that the partition result is more in line with the actual electrical characteristics of the power system, both the operating characteristic difference of different types of new energy units (photovoltaic and wind power) is considered, and the cooperative response ability of the units in the region is reflected. In combination with the unit parameters in the first partition and the equivalent tie line parameters between the partitions, a multi-region frequency response model is established to depict the frequency dynamic characteristics of each partition and the coupling relationship between the partitions. The model not only retains the individual characteristics of the units in the region, but also reflects the overall response law of the partition interconnection, thereby improving the prediction accuracy of the system frequency change trend. The virtual inertia configuration model constructed by taking the minimum cost as the objective function and taking the maximum frequency deviation and the frequency change rate as the constraint conditions can minimize the virtual inertia configuration cost of the photovoltaic and wind power on the premise of ensuring the system frequency safety, avoid the economic decline caused by excessive configuration of virtual inertia, and ensure the stability of the system in the multi-disturbance scene through the quantitative frequency index, so as to realize the balance between economy and safety. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the application or the prior art, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0011] Figure 1 A flowchart of a partition configuration method of virtual inertia of a new energy power system provided by the embodiment of the application.

[0012] Figure 2 A schematic diagram of a single-machine system frequency response model provided by the embodiment of the application.

[0013] Figure 3 A schematic diagram of a multi-zone frequency response model is provided for an embodiment of the present application.

[0014] Figure 4 A structural schematic diagram of a new energy power system is provided for an embodiment of the present application.

[0015] Figure 5 A result schematic diagram of an inertia configuration scheme under single disturbance and a set of multiple disturbances is provided for an embodiment of the present application.

[0016] Figure 6 A structural schematic diagram of a partition configuration device of virtual inertia of a new energy power system is provided for an embodiment of the present application.

[0017] Figure 7 A structural schematic diagram of an electronic device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the following describes in detail the specific implementation, structure, features and effects of the partition configuration method, device and equipment of virtual inertia of a new energy power system according to the present application, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0020] The following specifically describes the specific scheme of the partition configuration method, device and equipment of virtual inertia of a new energy power system according to the present application, with reference to the accompanying drawings.

[0021] As shown in Figure 1 The partition configuration method of virtual inertia of a new energy power system disclosed by the embodiments of the present application includes:

[0022] Step S101, partitioning the new energy power system according to the electrical distance between each generator node in the new energy power system and the association matrix between the network nodes and each generator node in the new energy power system, to obtain a plurality of first partitions, the generator nodes including photovoltaic units and wind power units.

[0023] Specifically, the generator node in the embodiment of the present application includes a photovoltaic unit and a wind turbine unit. The electrical distance between each generator node can be represented by a system admittance matrix. Each element in the association matrix between the network node and the generator node indicates the degree of association between the network node and each generator node.

[0024] In the present application, as an optional embodiment, the new energy power system is partitioned according to the electrical distance between each generator node in the new energy power system and the association matrix between the network node and each generator node in the new energy power system, to obtain a plurality of first partitions, including: partitioning the generator nodes in the new energy power system according to the electrical distance between each generator node, to obtain a plurality of second partitions; and dividing the network node into the second partition to which the generator node with the greatest impact on the network node belongs, according to the association matrix between the network node and each generator node and the second partition, to obtain a plurality of first partitions of the new energy power system, wherein the elements in the association matrix indicate the degree of association between the network node and each generator node, and the greater the element value, the greater the impact of the generator node corresponding to the element value on the network node.

[0025] Specifically, the embodiment of the present application first partitions the generator nodes according to the electrical distance between each generator node, and then divides the network node into the partition to which the generator node with the greatest impact on the network node belongs, to complete the partition of the network node. Through multi-level partitioning, accurate partitioning of the new energy power system can be achieved. In the partitioning of the generator nodes, as an optional embodiment of the present application, the new energy power system is partitioned according to the electrical distance between each generator node in the new energy power system, to obtain a plurality of second partitions, including: determining the admittance matrix of the new energy power system according to the self-admittance of the generator node and the network node and the mutual admittance between the generator node and the network node; modifying the admittance matrix to obtain a modified admittance matrix, and incorporating the generator node into the modified admittance matrix to construct a diagonal matrix, and determining the system admittance augmented matrix of the generator node based on the diagonal matrix; reordering the rows and columns of the system admittance augmented matrix after price reduction processing, to establish the system network equation between the modified system admittance augmented matrix, the current matrix of the generator node and the disturbance occurrence node in the new energy power system, and the voltage matrix of the generator node and the network node; simplifying the modified system admittance augmented matrix based on the system network equation, to obtain a system admittance matrix that only retains the generator node, and taking the system admittance matrix as the electrical distance; and clustering the system admittance matrix to obtain a plurality of second partitions of the generator nodes in the new energy power system.

[0026] Specifically, the embodiment of the present application assumes that the number of generator nodes in the new energy power system is ng The number of network nodes is denoted as N. L The self-admittance of the generator node in this embodiment of the invention is denoted as Y. GG The self-admittance of a network node in this embodiment of the invention is denoted as Y. LL The mutual admittance between the generator node and the network node can be expressed as Y. GL The mutual admittance between network nodes and generator nodes can be expressed as Y. LG In this embodiment of the invention, the admittance matrix Y of the new energy power system is expressed as follows:

[0027]

[0028] Furthermore, in order to improve the accuracy of the admittance matrix of the new energy power system, this embodiment of the invention uses ground admittance to represent the loads connected to network nodes and generator nodes, and also uses Y... LL The corresponding diagonal components are corrected to obtain the corrected admittance matrix. The corrected admittance matrix is ​​expressed by the following formula:

[0029]

[0030] In the above formula, Y′ represents the corrected admittance matrix. LL This represents the self-admittance of the network node after correction. The remaining parameters are consistent with those in the above formula and can be referenced interchangeably; further details are omitted here from the embodiments of this invention.

[0031] Furthermore, after obtaining the corrected admittance matrix, this embodiment of the invention incorporates the generator node into the corrected admittance matrix to construct N. g *N g A diagonal matrix Y in dimensionality EE Based on Y EE The augmented admittance matrix of the system including the generator node can be obtained and then reduced in price, specifically expressed by the following formula:

[0032]

[0033] In the above formula, Y s Y represents the augmented system admittance matrix of the generator node. EE Y represents a diagonal matrix. ER Y RE Y RR These represent different parts of the system admittance augmentation matrix, where Y ER =[-Y EE 0]、Y RE =[-Y EE 0] T , Y EE YER Y RE Y RR The resulting matrix is ​​the system admittance augmentation matrix after price reduction. The meanings of the remaining parameters are the same as those of the corresponding parameters in the above formulas, and they can be referred to each other. Further details are omitted here from this embodiment of the invention.

[0034] Furthermore, in this embodiment of the invention, the system admittance augmentation matrix Y after price reduction processing is... s The rows and columns are rearranged to make the first N... g The row and column correspond to the generator node, the Nth g The +1 row and column correspond to the node where the disturbance occurs, and the other rows and columns correspond to the remaining generator nodes and load nodes. The system network equation of the new energy power system is established, specifically expressed by the following formula:

[0035]

[0036] In the above formula, I G This represents the current matrix injected into the renewable energy power system by generator nodes and disturbance-generating nodes. U G This represents the voltage matrix of the generator node. U L Y′ represents the voltage matrix of the network nodes. s Y′ represents the modified system admittance augmentation matrix. EE 、Y′ ER 、Y′ RE 、Y′ RR This forms the modified system admittance augmentation matrix.

[0037] Furthermore, in this embodiment of the invention, Y′ in the system network equation... s Partial simplification using the Kronecker product (Kron) eliminates U. L This allows us to obtain the system admittance matrix that retains only the generator nodes. In this embodiment of the invention, the system admittance matrix is ​​expressed as follows:

[0038]

[0039] In the above formula, Y″ s This represents the system admittance matrix. The meanings of the remaining parameters are the same as those of the corresponding parameters in the above formula, and will not be repeated here in this embodiment of the invention.

[0040] Furthermore, after obtaining the system admittance matrix, the K-means++ algorithm is used to cluster the system admittance matrix, thereby obtaining multiple second partitions of the generator nodes, and obtaining the partitioning results of the new energy power system containing only generator nodes.

[0041] Further, the embodiment of the present application obtains the partition result of the new energy power system by calculating the association matrix between the network nodes and the generator nodes and performing regional division of the network nodes. The embodiment of the present application represents the association matrix between the network nodes and the generator nodes as the following formula:

[0042]

[0043] In the above formula, M represents the association matrix between the network nodes and the generator nodes. The meanings of the remaining parameters are the same as those of the corresponding parameters in the above formula, and the embodiment of the present application will not be described here again.

[0044] Further, the embodiment of the present application performs normalization processing on each element in M to represent the association degree between the network nodes and the generator nodes. The closer the association between the network nodes and the generator nodes, the greater the corresponding element value, and the greater the influence of the network node on the generator node. Therefore, the embodiment of the present application divides each network node into the range of the motor that has the greatest influence on it according to the association matrix and the partition result of the second partition of the generator, and completes the partition of the entire new energy power system.

[0045] In step S102, a multi-region frequency response model of the new energy power system is determined according to the parameters of the photovoltaic generator and the wind turbine in each first partition and the equivalent tie line parameters between the first partitions.

[0046] Specifically, in the embodiment of the present application, after obtaining a plurality of first partitions, each first partition contains at least one wind turbine, photovoltaic generator and network node. The parameters of the photovoltaic generator and the wind turbine include but are not limited to load change per unit, unbalanced acceleration power per unit, frequency change per unit, motor inertia time constant, motor damping coefficient, motor governor adjustment coefficient, original motor high pressure cylinder work proportion, original motor reheating time constant, wind turbine power-frequency characteristic transfer function in low, medium and high wind speed zones, wind turbine power-frequency characteristic transfer function in low, medium and high wind speed zones, low, medium and high wind speed of wind turbine installed capacity proportion and low, medium and high wind speed of photovoltaic generator installed capacity proportion, etc. The equivalent tie line parameters between the first partitions are equivalent parameters for describing the overall characteristics of the interconnection line between different partitions in the new energy power system. They include but are not limited to equivalent resistance, equivalent reactance, equivalent susceptance, equivalent transmission capacity, equivalent voltage level, equivalent impedance angle, etc.

[0047] Further, as an optional embodiment of the present application, the multi-region frequency response model of the new energy power system is determined according to the parameters of the photovoltaic unit and the wind turbine unit in each first subregion and the equivalent tie-line parameters between the first subregions, and the method comprises the following steps: constructing a single-machine system frequency response model of virtual inertia control of each first subregion according to the operating parameters of the photovoltaic unit and the wind turbine unit in each first subregion; calculating the equivalent tie-line parameters between the first subregions according to the bus voltages at both ends of the tie-line between the first subregions, the reactance of the tie-line and the phase angle difference of the bus voltages at both ends; determining the tie-line transmission power between the first subregions according to the equivalent tie-line parameters between the first subregions and the equivalent center frequency of inertia of each first subregion; and splicing the single-machine system frequency response model of virtual inertia control of each first subregion based on the tie-line transmission power between the first subregions and the regional parameters of each first subregion to obtain the multi-region frequency response model of the new energy power system.

[0048] Specifically, the single-machine system frequency response model of virtual inertia control of each first subregion constructed by the embodiment of the present application is specifically as shown in Figure 2 Figure 2 A schematic diagram of a single-machine system frequency response model provided by the embodiment of the present application. Figure 2 In the formula, the single-machine system frequency response model comprises the power-frequency characteristic of the wind turbine unit, the power-frequency characteristic of the photovoltaic unit and the power-frequency characteristic of the generator, ΔP d is the load change unit; ΔP m is the unbalanced acceleration power unit. Δf is the frequency change unit. H is the inertia time constant of the motor. D is the damping coefficient of the motor. R is the speed regulator adjustment coefficient of the motor. F H is the high-pressure cylinder work proportion of the prime mover. T R is the reheating time constant of the prime mover. s represents the frequency of the motor. G l (s), G m (s), G h (s) are respectively the power-frequency characteristic transfer functions of the wind turbine unit in the low, medium and high wind speed regions. G pv (s) is the power-frequency characteristic transfer function of the photovoltaic unit. L w is the installed capacity proportion of the wind turbine unit. L pv is the installed capacity proportion of the photovoltaic unit, wherein the installed capacity proportions of the units in the low, medium and high wind speed regions are respectively L l , L m , L h , and L w = L l + L m + L h .

[0049] ​Further, the embodiment of the present application determines the tie line between each first partition and obtains the corresponding equivalent tie line parameter. Specifically, the embodiment of the present application calculates the equivalent tie line parameter according to the voltage of the two end buses of the tie line between each first partition, the reactance of the tie line and the phase angle difference of the voltage of the two end buses. As an optional embodiment of the present application, the calculation of the equivalent tie line parameter between each first partition according to the voltage of the two end buses of the tie line between each first partition, the reactance of the tie line and the phase angle difference of the voltage of the two end buses comprises: calculating the first product between the voltage of the two end buses of the tie line between the first partitions and the cosine value of the phase angle difference of the voltage of the two end buses; calculating the ratio between the first product and the reactance, and determining the second product between the ratio and the cosine value as the equivalent tie line parameter.

[0050] In the above formula, the embodiment of the present application calculates the equivalent tie line parameter according to the following formula:

[0051]

[0052] In the above formula, T ik represents the equivalent tie line parameter between the first partition i and the first partition k. U a represents the voltage of the bus at the a end of the two ends of the first partition i and the first partition k. U b represents the voltage of the bus at the b end of the two ends of the first partition i and the first partition k. X ab represents the reactance of the tie line L between the first partition i and the first partition k. δ ab represents the phase angle difference of the voltage of the two end buses of the tie line between the first partitions.

[0053] Further, after obtaining the equivalent tie line parameter between each first partition, the embodiment of the present application determines the tie line transmission power between each first partition according to the equivalent tie line parameter and the equivalent inertial center frequency of each first partition. As an optional embodiment of the present application, the determination of the tie line transmission power between each first partition according to the equivalent tie line parameter between each first partition and the equivalent inertial center frequency of each first partition comprises: obtaining the frequency difference value by time-integrating and then differencing the equivalent inertial center frequency of any two first partitions; and determining the third product between the predetermined value, the frequency difference value and the equivalent tie line parameter between any two first partitions as the tie line transmission power between any two first partitions.

[0054] Specifically, the predetermined value in the embodiment of the present application can be determined according to the actual situation, and the value in the embodiment of the present application is 2π. The embodiment of the present application calculates the tie line transmission power according to the following formula:

[0055] ΔP tik = 2πT ik (∫Δf i dt-∫Δfk dt)

[0056] In the above formula, ΔP tik T represents the transmission power of the tie line between the first partition i and the first partition k. ik This represents the equivalent link parameter between the first partition i and the first partition k. Δf i Let Δf represent the equivalent inertial center frequency of the first partition i. k This represents the equivalent inertial center frequency of the first partition k.

[0057] Furthermore, after obtaining the transmission power of the tie lines between each first zone, the single-machine system frequency response models of the virtual inertia control of each first zone are spliced ​​together according to the transmission power of the tie lines between each first zone and the regional parameters of each first zone to obtain the multi-region frequency response model of the new energy power system. For example, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of a multi-region frequency response model provided in an embodiment of the present invention. Figure 3 In this embodiment of the invention, taking the first partition i, the first partition l, and the first partition k as examples, the single-machine system frequency response models of the above three partitions are spliced ​​together to obtain the multi-region frequency response models of the above three regions. Wherein, ΔP di ΔP represents the per-unit value of the disturbance experienced by the first partition i (i.e., region i in the figure). tki This indicates that after the disturbance, the equivalent rated capacity of the first partition i is used as the benchmark, and the per-unit value of the power variable injected into the first partition k is the same as that of the first partition i. In this embodiment of the invention, the equivalent rated capacity can be determined based on the benchmark capacity of each first partition. In this embodiment of the invention, the equivalent rated capacity of the first partition i is denoted as S. iN The equivalent rated capacity of the first partition k is denoted as S. kN S iN S is the sum of the rated capacities of all synchronous motors in the first partition i. kN The sum of the rated capacities of all synchronous motors in the first zone k is used. Then, based on the principle that the tie-line power of the first zone i and the first zone k are equal, the per-unit value ΔP of the power variable injected into the first zone i by the first zone k after the disturbance is calculated. tki =a ik *ΔP tik aik is the conversion factor for the power of the tie line between the first partition i and the first partition k.

[0058] ΔP tikP represents the power variable per unit of the first partition i injected into the first partition k after the disturbance. In this way, the conversion coefficient is determined by the capacity of each partition, and the power variable per unit of the first partition i injected into the first partition k is further determined, so that more accurate power variable per unit can be obtained for different partitions, and the accuracy of model construction is improved.

[0059] Further, Figure 3 In the formula, the meanings of various parameters are as follows: ΔP tkl P represents the power variable per unit of the first partition k injected into the first partition l after the disturbance. ΔP til P represents the power variable per unit of the first partition i injected into the first partition l after the disturbance. P mi G represents the mechanical power of the first partition i. G wi (s) represents the power-frequency characteristic transfer function of the wind turbine of the i-th first partition. G pvi (s) represents the power-frequency characteristic transfer function of the photovoltaic turbine of the i-th first partition. H i H represents the motor inertia time constant of the i-th first partition. D i D represents the motor damping coefficient of the i-th first partition. Δf i K represents the frequency change per unit of the i-th first partition. K mi F represents the mechanical power gain coefficient of the i-th first partition. F Hi T represents the turbine high-pressure cylinder work proportion of the i-th first partition. T Ri R represents the turbine reheating time constant of the i-th first partition. R i s represents the governor adjustment coefficient of the i-th first partition. s represents the Laplace operator, which is used for the representation of the transfer function. T il K represents the synchronization time constant related parameter between the i-th first partition and the k-th first partition. It is worth noting that, Figure 3 In the formula, the meanings of various parameters in the first partition l (i.e., the area l) and the first partition k (i.e., the area k) can refer to the above explanation of the first partition i. In order to ensure the brevity of the file, the embodiments of the present application will not be described here.

[0060] In step S103, a target function of virtual inertia configuration of the photovoltaic turbine and the wind turbine in the multi-disturbance scenario is constructed with the minimum cost of the new energy power system as the target, frequency maximum deviation and frequency change rate are taken as quantitative indexes, and constraint conditions of the virtual inertia configuration of the photovoltaic turbine and the wind turbine in the multi-disturbance scenario are determined.

[0061] Specifically, the embodiment of the present application considers the economy of the virtual inertia configuration of the new energy power system, constructs a target function of the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios with the minimum cost of the new energy power system as the target. As an optional embodiment of the present application, the target function of the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios with the minimum cost of the new energy power system as the target includes: determining the output power of the wind turbine unit and the photovoltaic unit under each disturbance in the new energy power system and the cost coefficient of the unit output power; determining the total cost of the photovoltaic unit and the wind turbine unit under each disturbance based on the output power of the wind turbine unit and the photovoltaic unit, the cost coefficient of the unit output power, and the installed capacity of the wind turbine unit and the photovoltaic unit in the new energy power system; determining the total output cost of the wind turbine unit and the photovoltaic unit under the multiple disturbance set based on the probability of each disturbance occurring in the multiple disturbance set and the total cost; and determining the target function with the minimum total output cost.

[0062] Specifically, the embodiment of the present application uses the following formula to represent the target function:

[0063]

[0064] In the above formula, C R represents the total output cost of the wind turbine unit and the photovoltaic unit under the multiple disturbance set.

[0065] η j represents the probability of the jth disturbance occurring in the multiple disturbance set. J is the total number of disturbances in the multiple disturbance set.

[0066] CR j is the total cost of the photovoltaic unit and the wind turbine unit under the jth disturbance. E wi represents the output power of the ith wind turbine unit. E pvi represents the output power of the ith photovoltaic unit. γ wi represents the cost coefficient of the unit output power of the ith wind turbine unit. γ pvi represents the cost coefficient of the unit output power of the ith photovoltaic unit. N w represents the installed capacity of the wind turbine unit in the new energy power system. N pv represents the installed capacity of the photovoltaic unit in the new energy power system. min C R represents that the total output cost of the wind turbine unit and the photovoltaic unit under the multiple disturbance set takes the minimum value.

[0067] Further, the constraint condition in the embodiment of the present application is represented by the following formula:

[0068] -RoCoF lim ≤ RoCoF ≤ RoCoF lim

[0069]

[0070] In the above formula, -RoCoF lim represents the lower limit value of the frequency change rate of each sub-zone in the new energy power system. RoCoF lim represents the upper limit value of the frequency change rate of each sub-zone in the new energy power system.

[0071] RoCoF represents the frequency change rate of each sub-zone in the new energy power system. represents the lower limit value of the maximum frequency deviation of each sub-zone in the new energy power system. represents the upper limit value of the maximum frequency deviation of each sub-zone in the new energy power system. Af max represents the maximum frequency deviation of each sub-zone in the new energy power system.

[0072] Further, the embodiment of the present application combines the objective function and the constraint condition to obtain a sub-zone configuration model of the virtual inertia of the new energy power system in a multi-disturbance scene, and specifically uses the following formula to represent:

[0073]

[0074] In the sub-zone configuration model of the virtual inertia of the new energy power system in the multi-disturbance scene, each parameter can refer to the explanation and description of the corresponding parameter in the above formula, and can be mutually referred, and the present application embodiment will not be repeated here.

[0075] In step S104, a sub-zone configuration model of the virtual inertia of the new energy power system in a multi-disturbance scene is constructed based on the objective function and the constraint condition, and the virtual inertia is configured to the photovoltaic unit and the wind power unit based on the sub-zone configuration model of the virtual inertia of the new energy power system and the multi-zone frequency response model of the new energy power system.

[0076] Specifically, after obtaining the partition configuration model of the virtual inertia of the new energy power system in a multi-disturbance scenario, the virtual inertia is configured to the photovoltaic generator and the wind turbine based on the partition configuration model of the virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system. As an optional embodiment of the present application, configuring the virtual inertia to the photovoltaic generator and the wind turbine based on the partition configuration model of the virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system comprises: after initial assignment of the virtual inertia control coefficient of the multi-region frequency response model of the new energy power system, running to obtain the maximum change rate and the maximum offset of the inertia center frequency of each first partition; inputting the maximum change rate and the maximum offset of the inertia center frequency of each first partition into the partition configuration model to solve the partition configuration model and obtain the virtual inertia optimization configuration result, if the virtual inertia optimization configuration result does not satisfy the objective function and the constraint condition of the partition configuration model, then inputting the virtual inertia optimization configuration result into the multi-region frequency response model of the new energy power system again to run and obtain the updated maximum change rate and the updated maximum offset of the inertia center frequency of each first partition, inputting the updated maximum change rate and the updated maximum offset of the inertia center frequency of each first partition into the partition configuration model again to solve the partition configuration model, and repeating the above steps until the virtual inertia optimization configuration result satisfies the objective function and the constraint condition of the partition configuration model.

[0077] Specifically, the genetic algorithm and the multi-region frequency response model of the new energy power system designed for joint debugging and solving are respectively built on the MATLAB and Simulink platforms. Then, the genetic algorithm based on Matlab / Simulink joint debugging is run to obtain the optimal virtual inertia configuration scheme with the minimum configuration cost while meeting the safety constraints of the frequency of each partition under the multi-disturbance set. The solving process in the embodiment of the present application comprises: under the multi-disturbance set, the virtual inertia control coefficient of the multi-region frequency response model of the new energy power system built on the Simulink platform is assigned by the genetic algorithm based on the MATLAB platform, the Simulink platform is run in real time to obtain the maximum change rate and the maximum deviation of the inertia center frequency of each region, and then input into the genetic algorithm based on the MATLAB platform to solve the partition configuration model set in the above embodiment of the present application, and the joint debugging and solving are repeated until the frequency change rate and the offset of each partition in the new energy power system meet the safety constraints and the virtual inertia optimization configuration result with the minimum configuration cost is obtained.

[0078] Further, the embodiment of the present application also verifies the effectiveness of the virtual inertia optimization configuration result, thereby further improving the reliability and stability of the new energy power system. First, the embodiment of the present application measures the motor operating angular frequency and the inertia time constant of each partition after the actual new energy power system disturbance occurs under a plurality of disturbance sets, and the actual motor frequency maximum change rate and deviation, combines formula The inertia center frequency of each region can be calculated, thereby further obtaining the maximum change rate and deviation of the actual inertia center frequency of each region. Wherein, H i represents the inertia time constant of the i-th partition motor. ω i represents the operating angular frequency of the i-th generator. N g represents the number of generators. ω c represents the inertia center frequency. Further, the embodiment of the present application brings the above calculated virtual inertia optimization configuration result into the actual new energy power system, measures the motor frequency maximum change rate and deviation of each region after the disturbance occurs under a plurality of disturbance sets, combines formula The inertia center frequency maximum change rate and deviation of each partition are calculated, and the difference between the actual inertia center frequency maximum change rate and deviation is compared to verify the effectiveness of the configuration scheme. If the difference is greater than a threshold value, it means that the effectiveness of the configuration scheme is poor, otherwise.

[0079] The embodiment of the present application is aimed at the difference of disturbance probability of different partitions. The embodiment of the present application performs virtual inertia configuration under a plurality of disturbance scenes, so that the configuration result can adapt to complex and variable actual working conditions. By controlling the frequency quantization index in the constraint condition, it is ensured that the photovoltaic generator and the wind turbine can provide effective inertia support under various disturbances, suppress frequency fluctuation, and reduce the risk of local frequency overrun. Compared with the configuration scheme of a single disturbance scene, the overall anti-disturbance ability of the new energy power system can be significantly improved, breaking the limitation of traditional independent configuration of single type generator. Through the partition configuration model, the virtual inertia coordination relationship of photovoltaic generator and wind turbine is considered, the inertia response characteristics of the two are combined, the advantages of different types of new energy generators are complementary, the overall inertia resource of the power system is optimally utilized, and the anti-disturbance performance, stability and economic effectiveness of the power system are improved.

[0080] Furthermore, this embodiment of the invention partitions the system by using the electrical distance between generator nodes and the correlation matrix of network nodes. This partitioning result better reflects the actual electrical characteristics of the power system, taking into account the differences in operating characteristics of different types of new energy units (photovoltaic and wind power) and reflecting the coordinated response capability of units within the region. In addition, by combining the unit parameters within the first partition and the equivalent tie-line parameters between partitions, a multi-region frequency response model is established to characterize the frequency dynamics of each partition and the coupling relationship between partitions. This model retains the individual characteristics of units within the region while reflecting the overall response law of partition interconnection, improving the prediction accuracy of system frequency change trends. Using minimum cost as the objective function, and with maximum frequency offset and frequency change rate as constraints, the constructed virtual inertia configuration model minimizes the virtual inertia configuration cost for photovoltaic and wind power while ensuring system frequency safety. This avoids the economic decline caused by excessive virtual inertia configuration and ensures system stability under multiple disturbance scenarios through quantified frequency indicators, achieving a balance between economy and safety.

[0081] Furthermore, to further illustrate the technical solutions provided by the embodiments of the present invention, the embodiments of the present invention are verified in conjunction with specific application scenarios. An improved IEEE 10-machine 39-node system is built on the Matlab / Simulink platform, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of a new energy power system provided in an embodiment of the present invention. The new energy power system has a total of 9 generators, G2-G9 are grid-equivalent generators, G10 is a single-unit equivalent system of an adjacent system, the new energy power system has 34 transmission lines, 19 loads, a base capacity of 1000MVA, a base voltage of 345kV, and a total load active power of 8128.5MW. The synchronous generator G1 in the original 10-unit 39-node system is replaced with a 900MW wind farm cluster. The rated capacity of the low, medium, and high wind speed equivalent wind turbines is 300MW each. Photovoltaic units with capacities of 900MW and 1500MW are connected at nodes 7 and 24, respectively. Each wind turbine operates at a 10% load reduction level, and each photovoltaic unit operates at a 20% load reduction level. To verify that the present invention can comprehensively consider the frequency safety and stability constraints and the economic efficiency of inertia configuration in each region of the system, and simultaneously coordinate the inertia configuration of all wind and solar equipment with virtual inertia control in the system, the following operating conditions are set:

[0082] The surge 1045MW active load disturbance occurring at node 4, node 19 and node 26 constitutes a disturbance set, and three load disturbance probabilities are set as η1=η2=η3=1 / 3, the cost coefficients of wind turbines are γw1=γw2=γw3=0.52, and the cost coefficients of photovoltaic turbines are γpv1=γpv2=0.72. In addition, the inertia optimization configuration result based on the single disturbance of node 4 is recorded as working condition 1, the inertia optimization configuration result based on the single disturbance of node 19 is recorded as working condition 2, the inertia optimization configuration result based on the single disturbance of node 26 is recorded as working condition 3, and the inertia optimization configuration result based on the disturbance set of nodes 4, 19 and 26 is recorded as working condition 4, and the inertia optimization configuration is not performed and is recorded as the original working condition, the frequency change rate constraint of each region is [-1Hz / s, 1Hz / s], the maximum frequency deviation constraint is [-0.5Hz, 0.5Hz], and the inertia configuration scheme under single disturbance and under the disturbance set is optimized, as shown in Figure 5 Figure 5 The inertia configuration scheme under single disturbance and under the disturbance set provided by the embodiment of the present application is a result schematic diagram, Figure 5 In the inertia configuration scheme under single disturbance and under the disturbance set provided by the embodiment of the present application, Hw1 to Hw3 represent wind turbines, and Hpv1 and Hpv2 represent photovoltaic turbines. In the multi-disturbance scenario, the four inertia optimization configuration schemes are respectively substituted into the system to obtain the maximum frequency change rate and the maximum deviation amount before and after the inertia optimization configuration, as shown in the following table 1:

[0083] Table 1 Comparison of maximum frequency change rate and maximum deviation amount of each region under each inertia configuration scheme based on multi-disturbance scenario

[0084]

[0085] From the table, it can be concluded that:

[0086] When the 1045MW active load surges of the same probability occur at nodes 4, 19 and 26, the inertia configuration schemes based on working condition 1, working condition 2 and working condition 3, i.e. the inertia configuration schemes under single disturbance, all have out-of-limit conditions of frequency quantitative indicators, and cannot meet the frequency safety and stability constraints of the system under the multi-disturbance scenario, while under the inertia optimization configuration method based on the multi-disturbance set proposed by the present application, the maximum frequency deviation amount of each region is within the interval [-0.5Hz, 0.5Hz], and the frequency change rate is within the interval [-1Hz / s, 1Hz / s], which meets the set frequency safety threshold, as in the case of working condition 4 in table 1.

[0087] In addition, compared with the inertia optimized based on single disturbance, the inertia optimized based on the multi-disturbance set further considers the disturbance probability, is suitable for different disturbance forms, and meets the requirements of frequency safety indicators under each disturbance with less inertia setting, reduces the inertia support cost of new energy units, and is more suitable for the interconnected power system with large-scale new energy access today, and has more superiority and practicality compared with the existing method.​

[0088] Corresponding to the partition configuration method of the virtual inertia of the new energy power system provided by the above-mentioned embodiment, based on the same technical concept, the embodiment of the present application also provides a partition configuration device of virtual inertia of a new energy power system, which is used to execute the partition configuration method of virtual inertia of the new energy power system, Figure 6 The structural schematic diagram of a partition configuration device of virtual inertia of a new energy power system provided by an embodiment of the present application is shown in Figure 6 As shown, the partition configuration device of virtual inertia of the new energy power system comprises: a partition module 601, configured to partition the new energy power system according to the electrical distance between each generator node in the new energy power system and the association matrix between the network nodes and each generator node in the new energy power system, to obtain a plurality of first partitions, the generator node comprising a photovoltaic unit and a wind turbine; a determination module 602, configured to determine a multi-region frequency response model of the new energy power system according to the parameters of the photovoltaic unit and the wind turbine in each first partition and the equivalent tie-line parameters between each first partition; the determination module 602 is also configured to construct a target function of virtual inertia configuration of the photovoltaic unit and the wind turbine under a multi-disturbance scene with the minimum cost of the new energy power system as the target, to determine the constraint condition of the virtual inertia configuration of the photovoltaic unit and the wind turbine under the multi-disturbance scene with the maximum frequency deviation and the frequency change rate as the quantitative indicators; a configuration module 603, configured to construct a partition configuration model of virtual inertia of the new energy power system under the multi-disturbance scene based on the target function and the constraint condition, and to configure virtual inertia to the photovoltaic unit and the wind turbine based on the partition configuration model of virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system.

[0089] The partition configuration device of virtual inertia of the new energy power system provided by the embodiment of the present application is based on the same application concept as the partition configuration method of virtual inertia of the new energy power system provided by the embodiment of the present application, so the specific implementation of this embodiment can be referred to the implementation of the aforementioned partition configuration method of virtual inertia of the new energy power system, and has the same or similar beneficial effects, and the repeated parts will not be described herein.

[0090] Corresponding to the partition configuration method of the virtual inertia of the new energy power system provided by the above-mentioned embodiment, based on the same technical concept, the embodiment of the present application also provides an electronic device, which is used to execute the partition configuration method of virtual inertia of the new energy power system, Figure 7 The structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in Figure 7The electronic device can have a large difference due to different configurations or performances, and can include one or more processors 701 and memories 702 for storing computer programs executable on the processors 701 and the processors 701 for executing the programs stored on the memories 702 to implement the above Figure 1 The memories 702 can be temporary memories or persistent memories. The application programs stored in the memories 702 can include one or more modules (not shown in the figure), each of which can include a series of computer executable instructions in the electronic device.

[0091] Further, the processors 701 can be configured to communicate with the memories 702 to execute a series of computer executable instructions in the memories 702 on the electronic device. The electronic device can further include one or more power supplies 703, one or more wired or wireless network interfaces 704, one or more input / output interfaces 705, and one or more keyboards 706.

[0092] In particular, in the embodiment, the electronic device includes a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface, and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored on the memory to implement the above Figure 1 The steps in the method embodiments, and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present application will not be described here.

[0093] It should be noted that the electronic device provided by the embodiments of the present application and the partition configuration method of virtual inertia of the new energy power system provided by the embodiments of the present application are based on the same application concept, so the specific implementation of this embodiment can refer to the implementation of the aforementioned partition configuration method of virtual inertia of the new energy power system, and has the same or similar beneficial effects, and the repeated parts will not be described here.

[0094] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0095] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the differences from other embodiments.

Claims

1. A method for partitioning and configuring virtual inertia in a new energy power system, characterized in that, include: The new energy power system is partitioned according to the electrical distance between each generator node in the new energy power system and the correlation matrix between the network node and each generator node in the new energy power system, resulting in multiple first partitions. The generator nodes include photovoltaic units and wind turbine units. Based on the parameters of the photovoltaic units and wind turbines in each of the first zones and the equivalent tie line parameters between each of the first zones, the multi-region frequency response model of the new energy power system is determined. With the goal of minimizing the cost of the new energy power system, an objective function for the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios is constructed. The maximum frequency offset and the frequency change rate are used as quantitative indicators to determine the constraints on the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios. Based on the objective function and the constraints, a partitioned configuration model of the virtual inertia of the new energy power system under multiple disturbance scenarios is constructed. Based on the partitioned configuration model of the virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system, virtual inertia is configured for the photovoltaic units and wind turbine units.

2. The method for partitioning the virtual inertia of a new energy power system according to claim 1, characterized in that, The new energy power system is partitioned based on the electrical distance between generator nodes and the correlation matrix between network nodes and generator nodes in the new energy power system, resulting in multiple first partitions, including: The generator nodes in the new energy power system are divided into multiple second partitions based on the electrical distance between each generator node in the new energy power system. Based on the correlation matrix between network nodes and generator nodes in the new energy power system and the second partition, the network node is divided into the second partition to which the generator node with the greatest influence belongs, thus obtaining multiple first partitions of the new energy power system. The elements in the correlation matrix indicate the degree of correlation between the network node and each generator node. The larger the element value, the greater the influence of the generator node corresponding to that element value on the network node.

3. The method for partitioning the virtual inertia of a new energy power system according to claim 2, characterized in that, The process of partitioning the generator nodes in the new energy power system based on the electrical distance between them results in multiple second partitions, including: The admittance matrix of the new energy power system is determined based on the self-admittance of the generator nodes and network nodes and the mutual admittance between the generator nodes and network nodes in the new energy power system. The admittance matrix is ​​corrected to obtain a corrected admittance matrix. The generator node is incorporated into the corrected admittance matrix to construct a diagonal matrix. The system admittance augmentation matrix of the generator node is determined based on the diagonal matrix. After reducing the price of the system admittance augmentation moment, the rows and columns are reordered to establish the system network equations between the corrected system admittance augmentation matrix, the current matrices of the generator nodes and disturbance generation nodes in the new energy power system, and the voltage matrices of the generator nodes and network nodes. The modified system admittance augmented matrix is ​​simplified based on the system network equation to obtain a system admittance matrix that retains only the generator node, and the system admittance matrix is ​​used as the electrical distance. Clustering the system admittance matrix yields multiple second partitions for the generator nodes in the new energy power system.

4. The method for partitioning the virtual inertia of a new energy power system according to claim 1, characterized in that, The step of determining the multi-regional frequency response model of the new energy power system based on the parameters of the photovoltaic units and wind turbine units within each of the first zones and the equivalent tie line parameters between each of the first zones includes: Based on the operating parameters of the photovoltaic units and wind turbine units in each of the first partitions, a single-machine system frequency response model for virtual inertia control in each of the first partitions is constructed. The equivalent tie line parameters between each of the first partitions are calculated based on the bus voltages at both ends of the tie line between each of the first partitions, the reactance of the tie line, and the phase angle difference between the bus voltages at both ends. The transmission power of the tie line between each of the first partitions is determined based on the equivalent tie line parameters between each of the first partitions and the equivalent inertial center frequency of each of the first partitions. Based on the transmission power of the tie lines between each of the first partitions and the regional parameters of each of the first partitions, the single-system frequency response models of the virtual inertia control of each of the first partitions are spliced ​​together to obtain the multi-region frequency response model of the new energy power system.

5. The method for partitioning the virtual inertia of a new energy power system according to claim 4, characterized in that, The calculation of the equivalent tie-line parameters between each of the first partitions based on the bus voltages at both ends of the tie-line, the reactance of the tie-line, and the phase angle difference of the bus voltages at both ends includes: Calculate the first product between the bus voltages at both ends of the tie line between the first partitions, and the cosine of the phase angle difference between the two bus voltages; Calculate the ratio between the first product and the reactance, and determine the second product between the ratio and the cosine value as the equivalent tie-line parameter.

6. The method for partitioning the virtual inertia of a new energy power system according to claim 4, characterized in that, The step of determining the transmission power of the tie line between each of the first partitions based on the equivalent tie line parameters between each of the first partitions and the equivalent inertial center frequency of each of the first partitions includes: The frequency difference is obtained by time integration of the equivalent inertial center frequencies of any two first partitions and then subtraction. The third product of the predetermined value, the frequency difference, and the equivalent tie-line parameters between any two first partitions is determined as the tie-line transmission power between any two first partitions.

7. The method for partitioning the virtual inertia of a new energy power system according to claim 1, characterized in that, The objective function for constructing the virtual inertia configuration of the photovoltaic unit and wind turbine unit under multiple disturbance scenarios, with the goal of minimizing the cost of the new energy power system, includes: Determine the output power and cost coefficient per unit output power of wind turbines and photovoltaic units under each disturbance in the new energy power system. Based on the output power and cost coefficient per unit output power of the wind turbine and photovoltaic unit, as well as the installed capacity of the wind turbine and photovoltaic unit in the new energy power system, the total cost of the photovoltaic unit and wind turbine under each disturbance is determined. Based on the probability of each disturbance occurring within the multi-disturbance set and the total cost, the total output cost of the wind turbine and photovoltaic unit under the multi-disturbance set is determined; The objective function is to minimize the total output cost. The constraints include: the frequency change rate of each first partition is between the upper and lower limits of the frequency change rate, and the maximum frequency offset of each first partition is between the upper and lower limits of the maximum frequency offset.

8. The method for partitioning the virtual inertia of a new energy power system according to claim 1, characterized in that, The partitioned configuration model based on the virtual inertia of the new energy power system and the multi-regional frequency response model of the new energy power system, configuring virtual inertia for the photovoltaic units and wind turbine units includes: After initializing the virtual inertia control coefficients of the multi-region frequency response model of the new energy power system, the system is run to obtain the maximum rate of change and maximum offset of the inertia center frequency of each of the first partitions. The maximum rate of change and maximum offset of the inertia center frequency of each of the first partitions are input into the partition configuration model to solve the partition configuration model and obtain the virtual inertia optimization configuration result. If the virtual inertia optimization configuration result does not meet the objective function and constraints of the partition configuration model, the virtual inertia optimization configuration result is input into the multi-region frequency response model of the new energy power system again to obtain the updated maximum rate of change of the inertia center frequency and the updated maximum offset of each of the first partitions. The updated maximum rate of change of the inertia center frequency and the updated maximum offset of each of the first partitions are input into the partition configuration model again to solve the partition configuration model. The above steps are repeated until the virtual inertia optimization configuration result meets the objective function and constraints of the partition configuration model.

9. A partitioned configuration device for virtual inertia in a new energy power system, characterized in that, include: The partitioning module is used to partition the new energy power system according to the electrical distance between each generator node in the new energy power system and the correlation matrix between the network node and each generator node in the new energy power system, to obtain multiple first partitions. The generator nodes include photovoltaic units and wind turbine units. The determination module is used to determine the multi-region frequency response model of the new energy power system based on the parameters of the photovoltaic units and wind turbine units in each of the first partitions and the equivalent tie line parameters between each of the first partitions. The determining module is further configured to construct an objective function for the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios with the goal of minimizing the cost of the new energy power system, and to determine the constraints of the virtual inertia configuration of the photovoltaic unit and the wind turbine unit under multiple disturbance scenarios using the maximum frequency offset and frequency change rate as quantitative indicators. The configuration module is used to construct a partitioned configuration model of the virtual inertia of the new energy power system under multiple disturbance scenarios based on the objective function and the constraints, and to configure virtual inertia to the photovoltaic units and wind turbine units based on the partitioned configuration model of the virtual inertia of the new energy power system and the multi-region frequency response model of the new energy power system.

10. An electronic device, characterized in that, include: Processor and memory; wherein the memory is used to store computer programs that can run on the processor; A processor is used to execute a program stored in memory to implement the steps of the virtual inertia partitioning configuration method for a new energy power system as described in any one of claims 1-8.

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