Method and device for dynamic aggregation equivalent modeling of multiple new energy stations

Abstract

The application discloses a kind of to multiple new energy station carries out dynamic aggregation equivalent modeling method and device. Among them, method includes: respectively to the new energy station in each channel of multiple channel new energy station sequentially carries out aggregation equivalent modeling to the new energy station of aggregation;Before the aggregation equivalent modeling of each new energy station, the new energy station is judged feasibility, and under the condition that the new energy station has feasibility, the new energy station is aggregated and equivalent modeling is carried out;According to the aggregation equivalent modeling of all aggregable equivalent modeling new energy station in each channel, the channel aggregation equivalent model of each channel is determined;The aggregation equivalent model of all channels is aggregated and equivalent modeling, and the equivalent station model of multiple channel new energy station is determined.

Description

Technical Field

[0001] This invention relates to the field of power system simulation modeling technology, and more specifically, to a method and apparatus for dynamic aggregation and equivalent modeling of multiple new energy power plants. Background Technology

[0002] Analysis of subsynchronous / supersynchronous oscillations in new energy power systems often faces the "curse of dimensionality" problem. Because new energy equipment requires the establishment of high-order, complex dynamic models, time-domain simulations or linearization analyses of systems with a large number of new energy power plants suffer from limitations such as high model order and low analysis efficiency. Since the electrical components and control parameters of the units within a new energy power plant are basically the same, and the impedance of the bus lines between units is relatively small, equivalent impedance is typically achieved by connecting one unit in series with its equivalent impedance. This equivalent impedance method offers good accuracy.

[0003] As the core principle of frequency domain impedance analysis reveals, the evaluation criterion for multi-wind farm aggregation methods suitable for oscillation analysis lies in the degree to which the reduced-order model approximates the impedance characteristics of the original system. Furthermore, for simulation analysis purposes, the reduced-order model should ideally retain structural characteristics, allowing it to be reduced to a model of one or several wind turbines. Building upon the principle of equating multiple units within a single wind farm to a single equivalent unit, further aggregation and equivalence of multiple renewable energy wind farms is necessary to further reduce the system model order. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and apparatus for dynamic aggregation and equivalent modeling of multiple new energy power stations.

[0005] According to one aspect of the present invention, a method for dynamically aggregating and equating multiple renewable energy power plants is provided, comprising:

[0006] Aggregational equivalent modeling is performed on each of the multi-channel new energy power stations that are aggregated sequentially within each channel.

[0007] Before performing aggregated equivalent modeling on each new energy power station, a feasibility assessment is conducted on the new energy power station, and if the new energy power station is deemed feasible, aggregated equivalent modeling is performed on the new energy power station.

[0008] Aggregate equivalent modeling is performed on all new energy power stations within each channel that can be aggregated and modeled at an equivalent value, and the channel aggregate equivalent model for each channel is determined.

[0009] Aggregate equivalent models of all channels are used to determine the equivalent station model of multi-channel new energy power stations.

[0010] Optionally, the new energy power stations include direct-drive wind farms, photovoltaic power farms, and double-fed wind farms, and the direct-drive wind farms and photovoltaic wind farms are designated as Type I power stations, while the double-fed wind farms are designated as Type II power stations.

[0011] Optionally, before performing aggregated equivalent modeling on the new energy power stations that are sequentially aggregated within each channel of the multi-channel new energy power station, the following steps are also included:

[0012] Determine whether the types of power generation equipment in two new energy power stations that are equivalent to the same node are the same;

[0013] If the power generation equipment types in two new energy power stations that are equivalent to the same node are the same, determine whether the two new energy power stations are aggregateable; otherwise, determine whether they are not aggregateable.

[0014] Optionally, the feasibility assessment of the new energy power station includes:

[0015] Calculate the parameter difference of the new energy power stations to determine the parameter difference of the current aggregated new energy power stations;

[0016] If the parameter difference between the aggregated new energy power stations is less than or equal to the pre-set parameter difference threshold, then the aggregation of the aggregated new energy power stations is deemed feasible; otherwise, the aggregation of the aggregated new energy power stations is deemed not feasible.

[0017] Optionally, when the new energy power station is a Type I power station, the formula for calculating the parameter difference is:

[0018]

[0019] L t(k) =L f(k) +L Ta(k) +L Tb(k) +L Ln(k)

[0020]

[0021] Where k1, k2, and k3 are weighting coefficients, and their sum is 1; K p(k) Let K be the proportional gain of the converter current loop for the k-th power station. i(k) L is the integral gain of the converter current loop at the k-th power station; t(k) Let L be the equivalent series inductance of the kth power station, which is the converter filter inductance L. f(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum of;

[0022] When the renewable energy power station is a Type II power station, the formula for calculating the parameter difference is:

[0023]

[0024] L t(k) =L sσ(k) +L Ta(k) +L Tb(k) +L Ln(k)

[0025]

[0026] Where k1, k2, k3, and k4 are weighting coefficients, and their sum is 1; K rp(k) Let K be the proportional gain of the rotor converter current loop at the k-th power station. ri(k) Let L be the integral gain of the rotor converter current loop at the k-th power station. m(k) L is the magnetizing inductance of the induction motor at the k-th power station; t(k) Let L be the equivalent series inductance of the kth station, which is the stator leakage inductance of the induction motor. sσ(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum of .

[0027] Optionally, the operation of aggregated equivalent modeling is performed on each of the multi-channel renewable energy power stations that are aggregated sequentially within each channel, including:

[0028] Step 1: Integrate the branch inductance between the j-th node and the (j-1)-th node in the aggregated equivalent modeling channel into the j-th station, modify the station parameters, that is, make the channel equivalent to the (j-1)-th node. If j=1, the aggregation process of the channel ends; otherwise, proceed to Step 2.

[0029] Step 2: Let j = j-1. For the set of stations directly connected to the j-th node and the stations equivalent to the j-th node, if there are 2 or more Type I stations, let KI = 0; otherwise, let KI = -1. If KI = 0 and Type I station aggregation is feasible, let KI = 1. If there are 2 or more Type II stations, let KII = 0; otherwise, let KII = -1. If KII = 0 and Type II station aggregation is feasible, let KII = 1. If KI = 1 or KII = 1, proceed to Step 3; otherwise, end the aggregation process of this channel.

[0030] Step 3: If KI = 1, aggregate the Type I stations and calculate the first parameter of each equivalent station; if KII = 1, aggregate the Type II stations and calculate the second parameter of each equivalent station.

[0031] Optionally, the first parameter term includes K. p(eq) K i(eq) and L t(eq) The equivalent equation for frequency domain impedance is as follows:

[0032]

[0033] The above equation can be equivalent to:

[0034]

[0035] Where N is the number of stations to be aggregated, S (k) Let k be the rated capacity of the kth station;

[0036] The second parameter includes K. rp(eq)(m) K ri(eq)(m) L t(eq)(m) L m(eq)(m) and L r(eq)(m) The equivalent equation for frequency domain impedance is as follows:

[0037]

[0038] The above equation can be equivalent to:

[0039]

[0040] Among them, L r(k) It is the sum of the excitation inductance and rotor leakage inductance of the induction motor in the kth station.

[0041] Optionally, the operation of performing aggregated equivalent modeling on the aggregated equivalent models of all channels to determine the equivalent station model of the multi-channel renewable energy power station includes:

[0042] Using a preset parameter difference threshold value sequence, the equivalent station cluster number of Type I and Type II stations is calculated respectively;

[0043] Based on the equivalent number of station clusters for Type I and Type II stations, a clustering algorithm is used to group the Type I and Type II stations respectively.

[0044] Aggregate equivalent modeling is performed on the grouped Type I and Type II stations respectively, and the equivalent station parameters are calculated to determine the equivalent station model.

[0045] Optionally, the set of Type I stations is cI, and the set of Type II stations is cII. A preset threshold value sequence of parameter difference, diff_set2, is used, with its element values ​​set in ascending order and its length being less than the length of set cI or set cII. The operation of calculating the number of equivalent station clusters for Type I and Type II stations using the preset parameter difference threshold value sequence includes:

[0046] Calculating the parameter difference degree diff of each station in the set cI, if diff_set2[p-1]<diff<diff_set2[p], then the number of equivalent stations finally aggregated by this set is equal to p;

[0047] Calculating the parameter difference degree diff of each station in the set cII, if diff_set[p-1]<diff<diff_set[p], then the number of equivalent stations finally aggregated by this set is equal to p.

[0048] Optionally, according to the number of equivalent station groups of type I stations and type II stations, a clustering algorithm is used to group type I stations and type II stations respectively, which includes:

[0049] Randomly set the initial cluster center value sequence {x (av)(p)}, assign the data value sequence {x (k)} to the nearest cluster according to the distance from each cluster center, if the k-th station is assigned to the p-th cluster, it is expressed as k∈c (p) , where c(p) is the set of stations to be grouped;

[0050] After the completion of the current assignment, recalculate the center point for each cluster, that is

[0051]

[0052] where, N (p) is the number of stations in the p-th cluster;

[0053] Iteratively perform the two steps of allocating data points and updating cluster centers, and end the calculation when the specified number of iterations is reached;

[0054] Allocate N stations into P clusters, and obtain the number of stations N in each cluster (p) , and the station set c of each cluster (p) .

[0055] Optionally, performing aggregated equivalent modeling for the grouped type I stations and type II stations respectively, calculating equivalent station parameters, and determining the operation of the equivalent station model includes:

[0056] Aggregate the type I stations and calculate the first parameter item of the equivalent station of each type I station cluster;

[0057] Aggregate the type II stations and calculate the second parameter item of the equivalent station of each type II station cluster;

[0058] Determine the equivalent station model according to the aggregated type I stations and their first parameter items, and type II stations and their second parameter items.

[0059] According to another aspect of the present invention, an apparatus for dynamically aggregating and equating multiple renewable energy power plants is provided, comprising:

[0060] The aggregation equivalent modeling module is used to perform aggregation equivalent modeling on new energy power stations that are aggregated sequentially in each channel of a multi-channel new energy power station.

[0061] The feasibility assessment module is used to assess the feasibility of each new energy power station before performing aggregated equivalent modeling, and to perform aggregated equivalent modeling on the new energy power station if it is feasible.

[0062] The first determining module is used to perform aggregated equivalent modeling based on all new energy power stations that can be aggregated and modeled in each channel, and to determine the channel aggregated equivalent model for each channel.

[0063] The second determination module is used to perform aggregated equivalent modeling of all channels to determine the equivalent station model of multi-channel new energy power stations.

[0064] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0065] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0066] Therefore, the method for dynamic aggregation and equivalent modeling of multiple new energy power plants provided by this invention considers the feasibility of power plant equivalence during aggregation. For power plants containing different types of power generation equipment, their internal and external equivalences are differentiated, allowing for flexible and reasonable aggregation based on actual conditions. This achieves the technical effect of simplifying the equivalent modeling method, reducing the system model order, and improving analysis efficiency. Attached Figure Description

[0067] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0068] Figure 1 This is a flowchart illustrating a method for dynamically aggregating and equating multiple new energy power stations, provided by an exemplary embodiment of the present invention.

[0069] Figure 2This is another flowchart illustrating a method for dynamically aggregating and equating multiple new energy power stations, provided by an exemplary embodiment of the present invention.

[0070] Figure 3 This is a diagram of a multi-energy power station collection and transmission system provided in an exemplary embodiment of the present invention;

[0071] Figure 4a This is a partial structural diagram of a single channel provided in an exemplary embodiment of the present invention;

[0072] Figure 4b This is a schematic diagram of the aggregation of two stations provided in an exemplary embodiment of the present invention;

[0073] Figure 4c This is a schematic diagram of the merging of stations and lines provided in an exemplary embodiment of the present invention;

[0074] Figure 5a This is a schematic diagram of the system model before aggregation provided by an exemplary embodiment of the present invention;

[0075] Figure 5b This is a schematic diagram of the aggregated system model provided by an exemplary embodiment of the present invention;

[0076] Figure 6 This is a schematic diagram of the final equivalent model when some stations are not equivalent, provided by an exemplary embodiment of the present invention;

[0077] Figure 7 This is a schematic diagram comparing the frequency domain impedance of the grid-connected end before and after equivalence of multiple wind farms, provided by an exemplary embodiment of the present invention.

[0078] Figure 8 This is a schematic diagram of the structure of an apparatus for dynamically aggregating and equating multiple new energy power stations, provided in an exemplary embodiment of the present invention.

[0079] Figure 9 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0080] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0081] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0082] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0083] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0084] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0085] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0086] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0087] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0088] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0089] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0090] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0091] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0092] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0093] Exemplary methods

[0094] Figure 1 This is a flowchart illustrating a method for dynamically aggregating and equating multiple renewable energy power plants, provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as… Figure 1 As shown, the method 100 for dynamic aggregation and equivalent modeling of multiple new energy power plants includes the following steps:

[0095] Step 101: Perform aggregated equivalent modeling for each of the multi-channel new energy power stations that are aggregated sequentially within each channel;

[0096] Step 102: Before performing aggregated equivalent modeling for each new energy power station, a feasibility assessment is conducted for the new energy power station, and if the new energy power station is feasible, aggregated equivalent modeling is performed for the new energy power station.

[0097] Step 103: Perform aggregated equivalent modeling based on all new energy power stations within each channel that can be aggregated and modeled at equivalent value, and determine the channel aggregated equivalent model for each channel;

[0098] Step 104: Perform aggregated equivalent modeling on the aggregated equivalent models of all channels to determine the method for dynamic aggregated equivalent modeling of multiple new energy power stations and the equivalent power station model of multi-channel new energy power stations.

[0099] Specifically, the dynamic aggregation equivalent method for multiple new energy power stations described in this invention is a method for establishing a simplified equivalent model and obtaining model parameters for a local power system in which photovoltaic power stations, direct-drive wind farms, and doubly-fed wind farms are connected to the collection station through a tree-like channel.

[0100] (1) Some principles of equivalent modeling

[0101] 1) The overall process of this invention is as follows: Figure 2 As shown, the process presents a two-level tree-structured system. The collection station is connected to the first-level nodes of each channel via multiple parallel lines. After the first-level nodes, the channel no longer has radial outgoing lines, but instead connects to multiple new energy power stations only in series. It is worth noting that the method of this invention can be extended to multi-level tree-structured systems, achieving equivalence from all end nodes through the tree structure towards the root node.

[0102] 2) Direct-drive wind farms and photovoltaic power stations are classified as full-power converter stations and named as Type I stations; doubly-fed wind farms are named as Type II stations; the stations use a single equivalent unit to perform equivalent operation on all the original units.

[0103] 3) The following processing is applied to the SVG within the power station: For Type I power stations, since the control structure and parameters of the SVG are similar to those of direct-drive wind turbines and photovoltaic systems, in-station aggregation is first performed. This involves aggregating the equivalent generator and the SVG based on the single-unit equivalent of the generator group, obtaining equivalent parameters as new equivalent generator parameters. This equivalence process is similar to the aggregation method for Type I power stations in this invention. For Type II power stations, the first processing method is to ignore the SVG. In reality, the control structure and parameters of the SVG differ significantly from those of the doubly-fed induction generator (DFIG). Part of the reason for ignoring the SVG is that, in the analysis of common subsynchronous oscillations around 10Hz, the impedance of the DFIG is much smaller than that of the SVG. However, this processing would introduce certain errors in the supersynchronous frequency band. The second processing method for Type II power stations is to equate the parallel frequency domain impedance of the DFIG equivalent wind turbine and the SVG to a new DFIG equivalent wind turbine with new parameters. These parameters can be obtained by fitting using optimization methods. The aggregation process of the SVG and the equivalent power generation equipment will not be elaborated in detail here.

[0104] (2) Feasibility assessment of the aggregation of new energy power stations

[0105] Step 1: Determine based on station type. For two stations equivalent to the same node, determine their aggregation based on the type of power generation equipment within the station. Two stations of the same type are determined to be aggregable; otherwise, they are determined to be non-aggregable.

[0106] Step 2: Make a judgment based on parameter dispersion. For N similar stations equivalent to the same node, define diff as the parameter difference.

[0107] For Type I stations, the calculation formula is as follows:

[0108]

[0109] Where k1, k2, and k3 are weighting coefficients, and their sum is 1; K p(k) Let K be the proportional gain of the converter current loop for the k-th power station. i(k) L is the integral gain of the converter current loop at the k-th power station; t(k) Let L be the equivalent series inductance of the kth power station, which is the converter filter inductance L. f(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum (if any) is, i.e.

[0110] L t(k) =L f(k) +L Ta(k) +L Tb(k) +L Ln(k) (2)

[0111] The item labeled (av) represents the average value of that parameter, i.e.

[0112]

[0113] For Type II stations, the calculation formula is as follows:

[0114]

[0115] Where k1, k2, k3, and k4 are weighting coefficients, and their sum is 1; K rp(k) Let K be the proportional gain of the rotor converter current loop at the k-th power station. ri(k) Let L be the integral gain of the rotor converter current loop at the k-th power station. m(k) L is the magnetizing inductance of the induction motor at the k-th power station; t(k) Let L be the equivalent series inductance of the kth station, which is the stator leakage inductance of the induction motor. sσ(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum (if any) is, i.e.

[0116] L t(k) =L sσ(k) +L Ta(k) +L Tb(k) +L Ln(k) (5)

[0117] The item labeled (av) represents the average value of that parameter, i.e.

[0118]

[0119] When the parameter difference degree diff is not greater than the set threshold value diff_set1, the N stations are determined to be aggregable; otherwise, they are determined to be non-aggregable.

[0120] (3) Aggregated modeling of new energy power stations within the corridor

[0121] The following aggregation modeling process is performed on each channel. Suppose that the i-th sending channel contains N(i) new energy power stations and a total of N(i) level nodes. Number the channel nodes in order from the first end to the last end, let j = N(i), and aggregate them according to the following steps.

[0122] Step 1: Integrate the branch inductance between node j and node j-1 (node ​​0 is the aggregation station bus) into the j-th station, modify the station parameters, that is, make the channel equivalent to node j-1; if j=1, end the aggregation process of the channel, otherwise proceed to step 2.

[0123] Step 2: Let j = j-1; For the set of stations directly connected to the j-th node and the stations equivalent to the j-th node, if there are 2 or more Type I stations, let KI = 0, otherwise KI = -1; If KI = 0 and it is determined according to equation (1) that Type I station aggregation can be performed, let KI = 1; If there are 2 or more Type II stations, let KII = 0, otherwise KII = -1; If KII = 0 and it is determined according to equation (4) that Type II station aggregation can be performed, let KII = 1; If KI = 1 or KII = 1, proceed to step 3, otherwise end the aggregation process of the channel.

[0124] Step 3: If KI = 1, aggregate the Type I stations and calculate the first parameter K of each equivalent station using the following formula. p(eq) K i(eq) and L t(eq) The equivalent equation for frequency domain impedance is written as follows:

[0125]

[0126] Where N is the number of stations to be aggregated, S (k) Let be the rated capacity of the k-th station; simplifying this formula and deriving the calculation formulas for each parameter term, we get:

[0127]

[0128] If KII = 1, aggregate the type II stations and calculate the second parameter K for each equivalent station using the following formula. rp(eq)(m) K ri(eq)(m) Lt(eq)(m) , L m(eq)(m) and L r(eq)(m) . Write out the frequency-domain impedance equivalent equation as

[0129]

[0130] wherein, L r(k) is the sum of the excitation inductance and rotor leakage inductance of the induction motor at the k-th power station; after simplification, the calculation formulas for each parameter item are derived as

[0131]

[0132] After completing the above aggregation parameter calculation, return to step 1.

[0133] (4) Aggregation modeling of multi-channel new energy power stations

[0134] For all channels that have successfully completed aggregation modeling (not abnormally terminated), let the set of type I power stations be cI and the set of type II power stations be cII, and the following aggregation modeling process is performed.

[0135] Step 1: Calculate the number of equivalent power stations, that is, the number of clusters of power stations. A threshold sequence of parameter difference diff_set2 is preset, the elements of which are set in ascending order, and the length of which is less than the length of set cI or set cII; calculate the parameter difference diff of each power station in set cI according to formula (1), if diff_set2[p-1]<diff<diff_set2[p], then the number of equivalent power stations finally aggregated in this set is equal to p; calculate the parameter difference diff of each power station in set cII according to formula (4), if diff_set[p-1]<diff<diff_set[p], then the number of equivalent power stations finally aggregated in this set is equal to p.

[0136] Step 2: Cluster power stations by using the K-means algorithm.

[0137] 1) Preset clustering indicators; for type I power stations, the clustering indicator x (k) is expressed as

[0138] x (k) =h1K p(k) +h2L t(k) (11)

[0139] wherein, are weight coefficients, and the sum thereof is 1;

[0140] for type II power stations, the clustering indicator x (k) is expressed as

[0141] x (k) =h1K rp(k) +h2Lt(k) (12)

[0142] 2) Perform K-means iterative clustering. The specific steps are as follows: First, randomly set the initial sequence of cluster center values ​​{x}. (av)(p)}, the data value sequence {x (k) Stations are assigned to the nearest group based on their distance from the center point of each group. For example, if the k-th station is assigned to the p-th group, it is represented as k∈c. (p) , where c (p) First, the set of stations to be grouped; second, after this allocation is completed, the center point of each group is recalculated, i.e.

[0143]

[0144] Here N (p) Let N be the number of stations in the p-th cluster. Then, iteratively perform two steps: allocating data points and updating the cluster centroids. The calculation ends when the specified number of iterations is reached. Finally, N stations are allocated to P clusters, and the number of stations N in each cluster is obtained. (p) and the set of stations for each group c (p) .

[0145] Step 3: Perform aggregation and equivalent calculation on the two types of clustered stations, and calculate the equivalent station parameters. Aggregate Type I stations and calculate the equivalent station parameters for each Type I station group using Equation (8); aggregate Type II stations and calculate the equivalent station parameters for each Type II station group using Equation (10). For Type I stations, the parameters can be calculated based on the obtained L... t(k) The converter filter inductor L f(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Take a typical value and calculate the equivalent inductance L of the line according to equation (2). Ln(k) For Type II stations, the L data obtained can be used as a basis. t(k) The stator leakage inductance L of the induction motor sσ(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Take a typical value and calculate the equivalent inductance L of the line according to equation (5). Ln(k) .

[0146] In addition, 1) Figure 3 Diagram of a multi-energy power plant collection and transmission system, a typical 220kV multi-energy power plant collection and transmission system is shown below. Figure 3 As shown, multiple channels are connected to the aggregation station in a radial pattern, and multiple nodes of some channel lines are connected to new energy power stations. The new energy power stations mainly include new energy generating units (including box-type substations), SVG, and step-up transformers.

[0147] 2) Figures 4a-4c This is a schematic diagram of a single-channel aggregation process. Figure 4a This is the original structural diagram of two sections of track and two stations at the end of a certain corridor; Figure 4b To represent the equivalent model structure diagram of the second-level node at the end of the channel, the two stations connected to the second-level node and the first line segment are aggregated. Figure 4c The model structure diagram is equivalent to the third-level node at the end, which means merging the aforementioned equivalent station with the second section of the line; thereafter, the equivalent can continue to be derived from the first end of the channel according to a similar process.

[0148] 3) Figures 5a-5b This is a schematic diagram of the multi-channel aggregation process at the aggregation station. After aggregation is completed for each outgoing channel, Figure 5a A model structure diagram showing how each channel is equivalently represented to the aggregation station; Figure 5b To further aggregate the equivalent models of each channel to form a model structure diagram of one (or more) equivalent stations.

[0149] If some stations are unsuitable for aggregation equivalence due to significant differences in equipment type or parameters, the aggregation equivalence for that channel will cease. Taking the termination of aggregation for channel 3 as an example, the final equivalent model is as follows: Figure 6 As shown.

[0150] The following explanation uses a system with five directly driven wind farms connected to the grid as an example. Each of the five wind farms is stepped up and transmitted through lines before being connected in parallel to the same substation, forming a radial connection structure. Each wind farm is equivalent to a single wind turbine. Each equivalent wind farm is configured to operate under different working conditions and transmit through lines of varying lengths. Specific parameters are shown in Table 1. The leakage reactance of the step-up transformer is incorporated into the line reactance.

[0151] Table 1 Parameters for Each Wind Farm

[0152]

[0153]

[0154] Because of K p The parameters vary considerably, so the various stations are divided according to K. p The wind farms are divided into two groups: group A consists of wind farms numbered 1, 2, and 3, while group B consists of wind farms numbered 4 and 5. The equivalent parameters of the wind farms are obtained by weighting the line parameters, filter inductance, and control parameters according to their capacity, as shown in Table 2.

[0155] Table 2. Parameters of Equivalent Wind Farms

[0156]

[0157] After aggregating five wind farms into two equivalent wind farms, the overall frequency domain impedance characteristic curves of the grid connection ports before and after the aggregation were compared, as follows: Figure 7 As shown, the frequency domain impedance curves of the equivalent model and the full topology model generally agree well. This verifies the rationality of the technical solution of this invention.

[0158] This invention addresses the problem of simplified equivalent modeling for multiple renewable energy power plants. It achieves an engineering-feasible modeling method in three aspects: adaptability of the equivalent object, feasibility of the equivalent process, and accuracy of the equivalent model. Specifically, it manifests as follows:

[0159] (1) Adaptability of equivalent objects. The local power system, which is connected to the collection station in a radial manner by photovoltaic power plants, direct-drive wind farms, and doubly-fed wind farms, has the typical characteristics of general new energy grid-connected systems and meets the actual engineering requirements.

[0160] (2) Feasibility of the equivalent process. New energy power plants have complex connection configurations, including series and parallel connections. The impedance of the power plant's step-up transformer and the transmission lines affects the aggregation accuracy of the new energy power plant. Based on the system structure characteristics, this invention first performs aggregation and equivalent transformation on the connected power plants sequentially from the end to the beginning of a single transmission channel. Secondly, it further aggregates the equivalent power plants of multiple channels connected to the collection station. The implementation process is quite practical. During aggregation, the feasibility of power plant equivalence is considered. For power plants containing different types of power generation equipment, their internal and external equivalence are differentiated, allowing for flexible and reasonable aggregation based on actual conditions. In summary, this invention has good feasibility.

[0161] (3) Accuracy of the equivalent model. On the one hand, by analyzing the types of stations and the dispersion of parameters, the method provides a judgment on whether stations can be aggregated, avoids aggregating stations of different categories or with large differences, or increases the number of equivalent stations by grouping to maintain the accuracy of the equivalent model. On the other hand, for stations composed of two types of equipment, namely full-power converters and doubly fed wind turbines, the method proposes equivalent parameter calculation formulas that take into account frequency domain impedance. These formulas include the influence of unit control and electrical parameters, step-up transformer and line parameters, and have a relatively strict theoretical basis. Due to the dispersion of parameters and the cross-influence of multiple parameters, the error of aggregation between multiple stations is often difficult to control, but this invention ensures the accuracy of the equivalent model to a certain extent through the above methods.

[0162] Exemplary device

[0163] Figure 8 This is a schematic diagram of the structure of an apparatus for dynamically aggregating and equivalently modeling multiple new energy power stations, provided in an exemplary embodiment of the present invention. Figure 8 As shown, the device 800 includes:

[0164] The aggregation equivalent modeling module 810 is used to perform aggregation equivalent modeling on new energy power stations that are aggregated sequentially in each channel of a multi-channel new energy power station.

[0165] The feasibility assessment module 820 is used to assess the feasibility of each new energy power station before performing aggregated equivalent modeling, and to perform aggregated equivalent modeling on the new energy power station if it is feasible.

[0166] The first determining module 830 is used to perform aggregated equivalent modeling based on all new energy power stations that can be aggregated and modeled in each channel, and to determine the channel aggregated equivalent model for each channel.

[0167] The second determining module 840 is used to perform aggregated equivalent modeling of all channels, determine the method for dynamic aggregated equivalent modeling of multiple new energy power stations, and determine the equivalent power station model of multi-channel new energy power stations.

[0168] Optionally, the new energy power stations include direct-drive wind farms, photovoltaic power farms, and double-fed wind farms, and the direct-drive wind farms and photovoltaic wind farms are designated as Type I power stations, while the double-fed wind farms are designated as Type II power stations.

[0169] Optionally, before performing aggregated equivalent modeling on the new energy power stations that are sequentially aggregated in each channel of the multi-channel new energy power station, the device 800 further includes:

[0170] Determine whether the types of power generation equipment in two new energy power stations that are equivalent to the same node are the same;

[0171] If the power generation equipment types in two new energy power stations that are equivalent to the same node are the same, determine whether the two new energy power stations are aggregateable; otherwise, determine whether they are not aggregateable.

[0172] Optionally, the feasibility assessment module 820 includes:

[0173] Calculate the parameter difference of the new energy power stations to determine the parameter difference of the current aggregated new energy power stations;

[0174] If the parameter difference between the aggregated new energy power stations is less than or equal to the pre-set parameter difference threshold, then the aggregation of the aggregated new energy power stations is deemed feasible; otherwise, the aggregation of the aggregated new energy power stations is deemed not feasible.

[0175] Optionally, when the new energy power station is a Type I power station, the formula for calculating the parameter difference is:

[0176]

[0177] L t(k) =L f(k)+L Ta(k) +L Tb(k) +L Ln(k)

[0178]

[0179] Where k1, k2, and k3 are weighting coefficients, and their sum is 1; K p(k) Let K be the proportional gain of the converter current loop for the k-th power station. i(k) L is the integral gain of the converter current loop at the k-th power station; t(k) Let L be the equivalent series inductance of the kth power station, which is the converter filter inductance L. f(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum of;

[0180] When the renewable energy power station is a Type II power station, the formula for calculating the parameter difference is:

[0181]

[0182] L t(k) =L sσ(k) +L Ta(k) +L Tb(k) +L Ln(k)

[0183]

[0184] Where k1, k2, k3, and k4 are weighting coefficients, and their sum is 1; K rp(k) Let K be the proportional gain of the rotor converter current loop at the k-th power station. ri(k) Let L be the integral gain of the rotor converter current loop at the k-th power station. m(k) L is the magnetizing inductance of the induction motor at the k-th power station; t(k) Let L be the equivalent series inductance of the kth station, which is the stator leakage inductance of the induction motor. sσ(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Line equivalent inductance L Ln(k) The sum of .

[0185] Optionally, the aggregated equivalent modeling module 810 includes:

[0186] Step 1: Integrate the branch inductance between the j-th node and the (j-1)-th node in the aggregated equivalent modeling channel into the j-th station, modify the station parameters, that is, make the channel equivalent to the (j-1)-th node. If j=1, the aggregation process of the channel ends; otherwise, proceed to Step 2.

[0187] Step 2: Let j = j-1. For the set of stations directly connected to the j-th node and the stations equivalent to the j-th node, if there are 2 or more Type I stations, let KI = 0; otherwise, let KI = -1. If KI = 0 and Type I station aggregation is feasible, let KI = 1. If there are 2 or more Type II stations, let KII = 0; otherwise, let KII = -1. If KII = 0 and Type II station aggregation is feasible, let KII = 1. If KI = 1 or KII = 1, proceed to Step 3; otherwise, end the aggregation process of this channel.

[0188] Step 3: If KI = 1, aggregate the Type I stations and calculate the first parameter of each equivalent station; if KII = 1, aggregate the Type II stations and calculate the second parameter of each equivalent station.

[0189] Optionally, the first parameter term includes K. p(eq) K i(eq) and L t(eq) The equivalent equation for frequency domain impedance is as follows:

[0190]

[0191] The above equation can be equivalent to:

[0192]

[0193] Where N is the number of stations to be aggregated, S (k) Let k be the rated capacity of the kth station;

[0194] The second parameter includes K. rp(eq)(m) K ri(eq)(m) L t(eq)(m) L m(eq)(m) and L r(eq)(m) The equivalent equation for frequency domain impedance is as follows:

[0195]

[0196] The above equation can be equivalent to:

[0197]

[0198] Among them, L r(k) It is the sum of the excitation inductance and rotor leakage inductance of the induction motor in the kth station.

[0199] Optionally, the second determining module 840 includes:

[0200] Using a preset parameter difference threshold value sequence, the equivalent station cluster number of Type I and Type II stations is calculated respectively;

[0201] According to the number of equivalence station clusters of type I stations and type II stations, a clustering algorithm is used to cluster type I stations and type II stations respectively;

[0202] Perform aggregated equivalence modeling on the clustered type I stations and type II stations respectively, calculate equivalent station parameters, and determine an equivalent station model.

[0203] Optionally, the set of type I stations is cI, the set of type II stations is cII, and the threshold sequence diff_set2 of preset parameter difference degrees is set with its element values in ascending order, and the length of the sequence is less than the length of the set cI or the set cII. The operation of separately calculating the number of equivalent station clusters for type I stations and type II stations by using the preset parameter difference threshold sequence includes:

[0204] Calculate the parameter difference degree diff of each station in the set cI. If diff_set2[p-1]<diff<diff_set2[p], the number of equivalent stations that the set is finally aggregated into is p;

[0205] Calculate the parameter difference degree diff of each station in the set cII. If diff_set[p-1]<diff<diff_set[p], the number of equivalent stations that the set is finally aggregated into is p.

[0206] Optionally, clustering type I stations and type II stations respectively by using a clustering algorithm based on the number of equivalent station clusters of type I stations and type II stations includes:

[0207] Randomly set the initial cluster center value sequence {x (av)(p)}, assign the data value sequence {x (k)} to the nearest cluster according to the distance from each cluster center. If the k-th station is assigned to the p-th cluster, it is expressed as k∈c (p) , wherein c(p) is the set of stations to be clustered;

[0208] After the current allocation is completed, recalculate the center point for each cluster, that is

[0209]

[0210] wherein, N (p) is the number of stations in the p-th cluster;

[0211] Iterate the two steps of allocating data points and updating cluster centers, and end the calculation when the specified number of iterations is reached;

[0212] Allocate N stations into P clusters, and obtain the number of stations N in each cluster (p) , and the station set c of each cluster (p) .

[0213] Optionally, the operations of performing aggregated equivalent modeling on the grouped Type I and Type II stations, calculating equivalent station parameters, and determining the equivalent station model include:

[0214] Aggregate Type I stations and calculate the first parameter of the equivalent station for each Type I station group;

[0215] Aggregate the Type II stations and calculate the second parameter of the equivalent station for each Type II station group;

[0216] Based on the aggregated Type I station and its first parameter, and Type II station and its second parameter, the equivalent station model is determined.

[0217] Exemplary electronic devices

[0218] Figure 9 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 9 As shown, the electronic device 90 includes one or more processors 91 and memory 92.

[0219] The processor 91 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0220] The memory 92 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 91 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 93 and an output device 94, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0221] In addition, the input device 93 may also include, for example, a keyboard, a mouse, etc.

[0222] The output device 94 can output various information to the outside. The output device 94 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0223] Of course, for the sake of simplicity, Figure 9 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0224] Exemplary computer program products and computer-readable storage media

[0225] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0226] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0227] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0228] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0229] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0230] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0231] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0232] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0233] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0234] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for dynamic aggregation and equivalent modeling of multiple renewable energy power plants, characterized in that, include: Aggregational equivalent modeling is performed on each of the multi-channel new energy power stations that are aggregated sequentially within each channel. Before performing aggregated equivalent modeling on each new energy power station, a feasibility assessment is conducted on the new energy power station, and if the new energy power station is deemed feasible, aggregated equivalent modeling is performed on the new energy power station. Aggregate equivalent modeling is performed on all new energy power stations within each channel that can be aggregated and modeled at an equivalent value, and the channel aggregate equivalent model for each channel is determined. Aggregate the equivalent models of all channels to determine the equivalent station model of the multi-channel new energy power station; The procedures for conducting a feasibility assessment of the new energy power station include: The parameter difference degree of the new energy power station is calculated to determine the parameter difference degree of the current aggregated new energy power station; If the parameter difference of the aggregated new energy power station is less than or equal to a preset parameter difference threshold, the aggregation of the aggregated new energy power station is deemed feasible; otherwise, the aggregation of the aggregated new energy power station is deemed not feasible. When the new energy power station is a Type I power station, the formula for calculating the parameter difference is: in k 1, k 2 and k 3 represents the weighting coefficients, and their sum is 1; K p(k) Let be the proportional gain of the converter current loop for the k-th power station. K i(k) Let be the converter current loop integral gain of the k-th power station; L t(k) This is the equivalent series inductance of the k-th power station, and this inductance is the converter filter inductance. L f(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Equivalent inductance of the line L Ln(k) The sum of; When the new energy power station is a Type II power station, the formula for calculating the parameter difference is: in k 1, k 2, k 3 and k 4 represents the weighting coefficients, and their sum is 1; K rp(k) Let be the proportional gain of the rotor converter current loop at the kth power station. K ri(k) Let the integral gain of the rotor converter current loop be the value of the k-th power station. L m(k) Let be the magnetizing inductance of the induction motor at the kth power station; L t(k) This is the equivalent series inductance of the kth station, which is the stator leakage inductance of the induction motor. L sσ(k) Box-type transformer inductance L Ta(k) Step-up transformer inductance L Tb(k) Equivalent inductance of the line L Ln(k) The sum of .

2. The method according to claim 1, characterized in that, The new energy power stations include direct-drive wind farms, photovoltaic wind farms, and double-fed wind farms. The direct-drive wind farms and photovoltaic wind farms are designated as Type I power stations, and the double-fed wind farms are designated as Type II power stations.

3. The method according to claim 2, characterized in that, Before performing aggregated equivalent modeling on new energy power stations that are sequentially aggregated within each channel of a multi-channel new energy power station, the following is also included: Determine whether the types of power generation equipment in two new energy power stations that are equivalent to the same node are the same; If the power generation equipment types in two new energy power stations that are equivalent to the same node are the same, it is determined whether the two new energy power stations are aggregateable; otherwise, they are determined to be non-aggregable.

4. The method according to claim 2, characterized in that, The process involves performing aggregated equivalent modeling on each of the multi-channel renewable energy power stations, which are then aggregated sequentially within each channel. This includes: Step 1: Integrate the branch inductance between the j-th node and the (j-1)-th node in the aggregated equivalent modeling channel into the j-th station, modify the station parameters, that is, make the channel equivalent to the (j-1)-th node. If j=1, end the aggregation process of the channel; otherwise, proceed to Step 2. Step 2: Let j = j-1. For the set of stations directly connected to the j-th node and the stations equivalent to the j-th node, if there are 2 or more Type I stations, let KI = 0; otherwise, let KI = -1. If KI = 0 and Type I station aggregation is feasible, let KI = 1. If there are 2 or more Type II stations, let KII = 0; otherwise, let KII = -1. If KII = 0 and Type II station aggregation is feasible, let KII = 1. If KI = 1 or KII = 1, proceed to Step 3; otherwise, end the aggregation process of this channel. Step 3: If KI=1, aggregate the Type I stations and calculate the first parameter of each equivalent station; if KII=1, aggregate the Type II stations and calculate the second parameter of each equivalent station.

5. The method according to claim 4, characterized in that, The first parameter item includes K p(eq) , K i(eq) and L t(eq) The equivalent equation for frequency domain impedance is as follows: The above equation can be equivalent to: Where N is the number of stations to be aggregated. S (k) Let k be the rated capacity of the kth station; The second parameter includes K rp(eq)(m) , K ri(eq)(m) , L t(eq)(m) , L m(eq)(m) and L r(eq)(m) The equivalent equation for frequency domain impedance is as follows: The above equation can be equivalent to: in, L r(k) It is the sum of the excitation inductance and rotor leakage inductance of the induction motor in the kth station.

6. The method according to claim 4, characterized in that, The operation of performing aggregated equivalent modeling on all channels to determine the equivalent station model of the multi-channel new energy power station includes: Using a preset parameter difference threshold value sequence, the number of equivalent station clusters for the Type I station and the Type II station are calculated respectively. Based on the equivalent number of station clusters for Type I and Type II stations, a clustering algorithm is used to cluster the Type I and Type II stations respectively. Aggregate equivalent modeling is performed on the Type I and Type II stations after clustering, and the equivalent station parameters are calculated to determine the equivalent station model.

7. The method according to claim 6, characterized in that, The type I station set is denoted as cI, the type II station set is denoted as cII, and the preset parameter difference threshold value sequence is defined. diff_set2 The element values ​​are set in ascending order, and their length is less than the length of set cI or set cII. The operation of calculating the equivalent station cluster number for Type I and Type II stations using a preset parameter difference threshold value sequence includes: Calculate the parameter variability of each station in the ensemble cI. diff ,like diff_set2 [p-1] <diff<diff_set2 [p], then the final aggregate of this set into the number of equivalent stations is equal to p; Calculate the parameter variability of each station in set cII. diff ,like diff_set [p-1] <diff< diff_set If [p], then the set will eventually aggregate into the number of equivalent stations, which is equal to p.

8. The method according to claim 6, characterized in that, Based on the equivalent number of station clusters for Type I and Type II stations, a clustering algorithm is used to cluster the Type I and Type II stations respectively, including: Randomly set the initial sequence of group center values ​​{ x (av)(p) }, the sequence of data values ​​{ x (k) Assign them to the nearest group according to their distance from the center point of each group, where the th group is... k If a station is assigned to the p-th group, then it is represented as follows: ,in c ( p () represents the set of stations to be grouped; After this allocation is completed, the centroid of each group is recalculated, i.e. in, N (p) Let p be the number of stations in the p-th group; The calculation is performed iteratively in two steps: allocating data points and updating the group center point. The calculation ends when the specified number of iterations is reached. Will N The number of sites was assigned to P groups, and the number of sites in each group was obtained. N (p) and the collection of stations for each group. c (p) .

9. The method according to claim 6, characterized in that, The operation of performing aggregated equivalent modeling on the grouped Type I and Type II stations, calculating equivalent station parameters, and determining the equivalent station model includes: Aggregate the Type I stations and calculate the first parameter item of the equivalent station for each Type I station group; Aggregate the Type II stations and calculate the second parameter item of the equivalent station for each Type II station group; The equivalent station model is determined based on the aggregated Type I station and its first parameter, and the Type II station and its second parameter.

10. An apparatus for dynamically aggregating and modeling multiple renewable energy power plants, used to implement the method of claim 1, characterized in that, include: The aggregation equivalent modeling module is used to perform aggregation equivalent modeling on new energy power stations that are aggregated sequentially in each channel of a multi-channel new energy power station. The feasibility assessment module is used to assess the feasibility of each new energy power station before performing aggregated equivalent modeling, and to perform aggregated equivalent modeling on the new energy power station if it is feasible. The first determining module is used to perform aggregated equivalent modeling based on all new energy power stations that can be aggregated and modeled in each channel, and to determine the channel aggregated equivalent model for each channel. The second determining module is used to perform aggregated equivalent modeling of all channels to determine the equivalent station model of the multi-channel new energy power station.

11. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-9.

12. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-9.

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