A method and device for evaluating new energy carrying capacity of a multi-layer distribution network

Abstract

The application provides a kind of multi-level power distribution network new energy carrying capacity evaluation method and device.The method comprises: obtaining new energy data and load data in historical period;New energy data and load data are clustered, to determine a plurality of clustering scenarios;For each clustering scenario, with the maximum access capacity of multi-level power distribution network new energy as objective function, with the safe operation condition of multi-level power distribution network as constraint condition, the optimal solution of objective function is calculated, and the optimal solution includes the maximum access capacity of multi-level power distribution network, and the new energy access capacity of each node;Determine the minimum value of the maximum access capacity corresponding to each clustering scenario as the maximum access capacity of multi-level power distribution network new energy, and evaluate the new energy carrying capacity of multi-level power distribution network based on the new energy access capacity of each node corresponding to the minimum value.The application can improve the rationality of determining the maximum access capacity of new energy, and reasonably evaluate the new energy carrying capacity of multi-level power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power transmission and transformation technology, and in particular to a method and apparatus for evaluating the renewable energy carrying capacity of a multi-level distribution network. Background Technology

[0002] The large-scale and widespread integration of new energy sources, primarily photovoltaic and wind power, is an inevitable trend in the transformation and development of the power grid's energy sector. While the scale of new energy connections to distribution networks at all voltage levels continues to rise, significant issues remain regarding the layout of new energy installations.

[0003] Currently, the installed capacity of new energy sources is mostly determined by a percentage of the grid's energy consumption. However, due to significant seasonal variations in wind speed, solar radiation, and load, there is considerable uncertainty, leading to prominent issues in the absorption of new energy and significant curtailment of wind and solar power. Determining the capacity of new energy sources solely based on a percentage of the grid's energy consumption is unreasonable. Summary of the Invention

[0004] This invention provides a method and apparatus for evaluating the renewable energy carrying capacity of a multi-level distribution network, which can improve the rationality of determining the maximum renewable energy access capacity and reasonably evaluate the renewable energy carrying capacity of a multi-level distribution network.

[0005] In a first aspect, the present invention provides a method for evaluating the renewable energy carrying capacity of a multi-level distribution network, comprising: acquiring renewable energy data and load data within a historical period, wherein the renewable energy data includes wind speed data and / or solar radiation data; performing clustering processing on the renewable energy data and load data to determine multiple clustering scenarios; wherein the clustering scenarios are used to characterize the power generation characteristics of renewable energy generation; for each clustering scenario, taking the maximum renewable energy access capacity of the multi-level distribution network as the objective function and the safe operation conditions of the multi-level distribution network as the constraint condition, calculating the optimal solution of the objective function, wherein the optimal solution includes the maximum access capacity of the multi-level distribution network and the renewable energy access capacity of each node; determining the minimum value among the maximum access capacities corresponding to each clustering scenario as the maximum renewable energy access capacity of the multi-level distribution network, and evaluating the renewable energy carrying capacity of the multi-level distribution network based on the renewable energy access capacity of each node corresponding to the minimum value.

[0006] This invention provides a method for assessing the renewable energy carrying capacity of a multi-level distribution network. By performing cluster analysis on historical renewable energy and load data, various scenarios characterizing renewable energy generation are obtained. Within each cluster scenario, the maximum access capacity of the multi-level distribution network is calculated. Finally, the minimum value among the maximum access capacities corresponding to each cluster scenario is determined as the maximum renewable energy access capacity of the multi-level distribution network. By using the minimum value among all cluster scenarios to assess the maximum renewable energy carrying capacity of the multi-level distribution network, the curtailment of wind and solar power due to excess renewable energy generation in different scenarios or time periods is avoided. This invention improves the rationality of determining the maximum renewable energy access capacity and can reasonably assess the renewable energy carrying capacity of a multi-level distribution network.

[0007] In one possible implementation, clustering is performed on new energy data and load data to determine multiple clustering scenarios, including: Step 21, normalizing the new energy data and load data to obtain normalized data; Step 22, randomly selecting K days of data as K cluster centers based on the normalized data, where K is a positive integer; Step 23, using the K-means clustering algorithm to calculate the Euclidean distance from the data at each time point in the normalized data to the K cluster centers; and summing the multiple Euclidean distances to obtain the total Euclidean distance; Step 24, changing the positions of the K cluster centers, repeating step 23, comparing the total Euclidean distances obtained multiple times, and selecting the K days of data corresponding to the minimum value among the multiple total Euclidean distances as the cluster data; Step 25, calculating the clustering effectiveness index based on the clustering data; Step 26, changing the value of K, repeating steps 22-25 to obtain the clustering effectiveness index obtained multiple times, and selecting the K days corresponding to the maximum value among the multiple clustering effectiveness indices as the K clustering scenarios.

[0008] In one possible implementation, a clustering effectiveness index is calculated based on clustering data, including: determining the clustering effectiveness index based on the following formula;

[0009]

[0010] Where M represents the number of clusters, m represents the current class, and v i Indicates the class center, x represents the center point of the dataset. i Representing each point in the class, Tr(S) B ) represents the trace of the between-class deviation matrix, used to measure the separation of the dataset, Tr(S W ) represents the trace of the intra-class deviation matrix, used to measure the density of data within a class.

[0011] In one possible implementation, the objective function is to maximize the access capacity of new energy sources in the multi-level distribution network, and the optimal solution of the objective function is calculated under the constraints of the safe operation conditions of the multi-level distribution network. The steps include: Step 31: Based on the safe operation conditions of the multi-level distribution network, randomly determine the access capacity of new energy sources at each node in the multi-level distribution network; Step 32: Input the randomly determined access capacity of new energy sources at each node into the objective function to calculate the access capacity of new energy sources in each sub-region of the multi-level distribution network; Step 33: Based on the safe operation conditions of the multi-level distribution network, change the access capacity of new energy sources at each node in the multi-level distribution network, and repeat Step 32 to obtain the access capacity of new energy sources in each sub-region of the multi-level distribution network calculated through multiple iterations; Step 34: Sum the maximum values ​​of the access capacities of new energy sources in each sub-region to obtain the maximum access capacity of new energy sources in the multi-level distribution network. The optimal solution is the new energy access capacity of each node corresponding to the maximum access capacity of new energy sources in the multi-level distribution network.

[0012] In one possible implementation, the objective function is to maximize the access capacity of new energy sources in the multi-level distribution network, and the optimal solution of the objective function is calculated under the constraint of the safe operation conditions of the multi-level distribution network. The steps include: Step 1, initializing the distribution network parameters and the parameters of the alternating direction multiplier method; Step 2, solving the optimization subproblem of the upper-level distribution network and calculating the transmission power of the tie lines after decoupling between the upper and lower-level distribution networks; Step 3, solving the optimization subproblem of each lower-level distribution network based on the transmission power of the tie lines after decoupling between the upper and lower-level distribution networks; Step 4, updating the Lagrange multipliers; Step 5, determining whether the convergence accuracy has reached the set accuracy; if the set accuracy has been reached, the iteration process is exited and the optimal solution is output; if the set accuracy has not been reached, steps 2 to 5 are repeated until the convergence accuracy reaches the set accuracy.

[0013] In one possible implementation, the objective function is to maximize the access capacity of new energy sources in the multi-level distribution network, and the constraint is the safe operation conditions of the multi-level distribution network. The optimal solution of the objective function is calculated, including: Step 51, initializing the particle swarm optimization algorithm parameters and the access capacity of new energy sources at each node; Step 52, initializing the iteration count to 1; Step 53, inputting the access capacity of new energy sources at each node into the objective function to calculate the access capacity of new energy sources in the multi-level distribution network; Step 54, determining the global optimal solution, which is the access capacity of new energy sources at each node corresponding to the maximum access capacity of new energy sources in the multi-level distribution network; Step 55, determining whether the iteration count is greater than or equal to the set number. If yes, output the global optimal solution and exit the iteration process; if no, increment the iteration count by 1, update the access capacity of new energy sources at each node, and repeat steps 53-55 until the iteration process exits.

[0014] In one possible implementation, the method further includes: obtaining the actual installed capacity of new energy sources at each node in the multi-level distribution network; and formulating guidance measures for new energy sources in the multi-level distribution network based on the actual installed capacity of new energy sources at each node and the new energy access capacity at each node, wherein the guidance measures include the pending installed capacity of new energy sources at each node.

[0015] Secondly, embodiments of the present invention provide an evaluation device for the renewable energy carrying capacity of a multi-level distribution network, comprising: a communication module for acquiring renewable energy data and load data over a historical period, wherein the renewable energy data includes wind speed data and / or solar radiation data; a processing module for performing clustering processing on the renewable energy data and load data to determine multiple clustering scenarios; wherein the clustering scenarios are used to characterize the power generation characteristics of renewable energy generation; for each clustering scenario, taking the maximum renewable energy access capacity of the multi-level distribution network as the objective function and the safe operation conditions of the multi-level distribution network as the constraint condition, calculating the optimal solution of the objective function, wherein the optimal solution includes the maximum access capacity of the multi-level distribution network and the renewable energy access capacity of each node; determining the minimum value among the maximum access capacities corresponding to each clustering scenario as the maximum renewable energy access capacity of the multi-level distribution network, and evaluating the renewable energy carrying capacity of the multi-level distribution network based on the renewable energy access capacity of each node corresponding to the minimum value.

[0016] Thirdly, embodiments of the present invention provide an electronic device, characterized in that the electronic device includes a memory and a processor, the memory storing a computer program, and the processor being configured to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation thereof.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the method as described in the first aspect and any possible implementation thereof.

[0018] The technical effects of any of the implementation methods in the second to fourth aspects mentioned above can be found in the technical effects of the corresponding implementation methods in the first aspect, and will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1This is a flowchart illustrating a method for evaluating the renewable energy carrying capacity of a multi-level distribution network, as provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the change of a clustering effectiveness index provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of wind speed data in a typical scenario provided by an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of illumination data in a typical scenario provided by an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of load data in a typical scenario provided by an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of a two-level regional power distribution network provided in an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram of an adjacent sub-region decoupling process provided by an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of a node in a regional power distribution network model provided in an embodiment of the present invention;

[0028] Figure 9 This is a schematic diagram of the maximum access capacity of new energy sources in various scenarios of a regional power distribution network provided by an embodiment of the present invention;

[0029] Figure 10 This is an iterative process diagram of the maximum access capacity of new energy sources in distribution networks at all levels provided in the embodiments of the present invention;

[0030] Figure 11 This is a power transmission curve of the tie line provided in an embodiment of the present invention;

[0031] Figure 12 This is a comparison chart of the maximum access capacity of new energy sources under different evaluation strategies in the scenarios provided by the embodiments of the present invention;

[0032] Figure 13 This is a comparison chart of the optimization time of different evaluation strategies provided in the embodiments of the present invention;

[0033] Figure 14 This is a schematic diagram of the structure of an evaluation device for the new energy carrying capacity of a multi-level distribution network provided in an embodiment of the present invention;

[0034] Figure 15 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0035] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0036] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document 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 alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0037] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0038] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0040] As described in the background section, there is a problem with the unreasonable distribution of new energy access capacity in multi-level distribution networks.

[0041] To address the aforementioned technical problems, this invention provides a method for assessing the renewable energy carrying capacity of a multi-level distribution network. The executing entity is an assessment device for the renewable energy carrying capacity of a multi-level distribution network, and the method includes:

[0042] S101. Obtain new energy data and load data within a historical period.

[0043] Among them, new energy data includes wind speed data and / or solar radiation data.

[0044] For example, the evaluation device can acquire new energy data and load data from the year prior to the current time.

[0045] S102. Perform clustering processing on new energy data and load data to determine multiple clustering scenarios.

[0046] Among them, clustering is used to characterize the power generation characteristics of new energy power generation.

[0047] As one possible implementation, the evaluation device can normalize the annual operating history data sequences of wind speed, light intensity, and load, and transform them into a clusterable data matrix for simultaneous clustering.

[0048] For example, the evaluation device can be based on the formula Calculate normalized data for wind speed, sunlight, and load.

[0049] Where x represents the data at time t; x min x represents the minimum value of various data types; max x represents the maximum value of various data types; * This represents the normalized value of the data.

[0050] It should be noted that the clusterable data matrix is ​​a matrix that combines normalized wind, solar, and load data into a single matrix with a dimension of 365*72.

[0051] As one possible implementation, the evaluation device can determine multiple clustering scenarios through steps 21-26.

[0052] Step 21: Normalize the new energy data and load data to obtain normalized data.

[0053] Step 22: Based on the normalized data, randomly select K days of data as K cluster centers.

[0054] Where K is a positive integer. The range of values ​​for the cluster number K is... Where N is the number of data sequence samples. For example, there are 365 samples for one year.

[0055] In some embodiments, the data for each day of the K days may include M time points. The control device may randomly select M time points for each day's data.

[0056] Step 23: Use the K-means clustering algorithm to calculate the Euclidean distance from the data at each time point in the normalized data to the K cluster centers; and sum the multiple Euclidean distances to obtain the total Euclidean distance.

[0057] Step 24: Change the positions of the K cluster centers, repeat step 23, compare the total Euclidean distances calculated multiple times, and select the data at the K time points corresponding to the minimum total Euclidean distance among the multiple total Euclidean distances as the cluster data.

[0058] Step 25: Calculate the clustering effectiveness index based on the clustering data.

[0059] In some embodiments, clustering effectiveness metrics may include the Dunn index, silhouette coefficient, Davidson-Bolding index, CH index, XB index, and FBM index.

[0060] The CH index measures the density within a cluster by calculating the sum of squared distances between each point in the cluster and the cluster center, and measures the dispersion of the dataset by calculating the sum of squared distances between each cluster center and the dataset. The CH index is derived from the ratio of separation to density. A larger CH value indicates that the clusters are more tightly packed and more dispersed between clusters, resulting in better clustering performance.

[0061] For example, the evaluation device can determine the CH index based on the following formula, thereby determining the clustering effectiveness index.

[0062]

[0063] Where M represents the number of clusters, m represents the current class, and v i Indicates the class center, x represents the center point of the dataset. i Representing each point in the class, Tr(S) B ) represents the trace of the between-class deviation matrix, used to measure the separation of the dataset, Tr(S W ) represents the trace of the intra-class deviation matrix, used to measure the density of data within a class.

[0064] Step 26: Change the K value and repeat steps 22-25 to obtain the clustering effectiveness index calculated multiple times. Select the K time points corresponding to the maximum value among the multiple clustering effectiveness indices as K clustering scenarios.

[0065] like Figure 2 As shown, different K values ​​correspond to different CH indices. When K is 4, the CH index reaches its maximum value, and K=4 is the optimal number of clusters. Figure 3 The data represents wind speed data for four typical scenarios when K is the optimal number of clusters. Figure 4 The illumination data are for four typical scenarios when K is the optimal number of clusters. Figure 5 The data represents the load data for four typical scenarios when K is the optimal number of clusters.

[0066] S103. For each clustering scenario, take the maximum access capacity of new energy in the multi-level distribution network as the objective function and the safe operation conditions of the multi-level distribution network as the constraint condition, and calculate the optimal solution of the objective function.

[0067] The optimal solution includes the maximum access capacity of the multi-level distribution network and the new energy access capacity of each node.

[0068] In some embodiments, the objective function can be expressed as the following formula.

[0069]

[0070] in, Let be the photovoltaic access capacity at the upstream grid node i of the s-th scenario; Let be the wind power access capacity at the i-th upstream grid node of the s-th scenario; N1 represents the photovoltaic grid connection capacity at the lower-level grid node j in the s-th scenario; N1 represents the number of upper-level grid nodes; N 2,n N1 represents the number of nodes in the nth lower-level power grid; N2 represents the number of lower-level power grids connected to the upper-level power grid.

[0071] In some embodiments, the safe operation conditions of a multi-level distribution network include power flow constraints, node voltage constraints, upper and lower limits of tie line power transmission constraints, tie line power backfeed probability constraints, and new energy access capacity constraints at each node.

[0072] For example, power flow constraints may include power flow constraints of the upstream power grid and power flow constraints of the upstream power grid.

[0073] For example, the power flow constraints of the upstream power grid can be expressed as the following formula.

[0074]

[0075] For example, the power flow constraints of the upstream power grid can be expressed as the following formula.

[0076]

[0077] Among them, among them, Let be the active power of the renewable energy source at node i of the upstream power grid in the s-th scenario. Let i be the reactive power of the new energy source at node i of the upper-level power grid in the s-th scenario; Let be the active power of the load at node i of the upstream power grid in the s-th scenario. G represents the reactive power of the load at node i of the upstream power grid in the s-th scenario; 1ij B is the conductance between upstream power grid nodes i and j. 1ij θ represents the susceptance between upstream grid nodes i and j. 1ijV is the voltage phase angle between upstream grid nodes i and j. 1i Let V be the node voltage at node i of the upstream power grid in the s-th scenario. 1j Let j be the node voltage at the upstream power grid node of the s-th scenario. Let be the active power of the renewable energy source at node j of the lower-level power grid in the s-th scenario. Let J be the reactive power of the new energy source at node j of the lower-level power grid in the s-th scenario. Let be the active power of the load at node j of the next lower-level power grid in the s-th scenario. Let G be the reactive power of the load at node j of the lower-level power grid in the s-th scenario. 2ij B is the conductance between lower-level grid nodes i and j. 2ij θ represents the susceptance between lower-level grid nodes i and j. 2ij V represents the voltage phase angle between downstream grid nodes i and j. 2i Let V be the node voltage at node i in the lower-level power grid of the s-th scenario. 2j Let be the node voltage at node j of the lower-level power grid in the s-th scenario.

[0078] For example, node voltage constraints can be expressed as the following formula.

[0079] V i,min ≤V i,s ≤V i,max ;

[0080] Among them, V i,s Let V be the voltage amplitude of the s-th scene node i. i,min Minimum voltage at node i, V i,max Let be the maximum voltage at node i.

[0081] For example, the upper and lower limits of tie line power transmission can be expressed as the following formula.

[0082] P l·i,min ≤P l·i,s,t ≤P l·i,max ;

[0083] Among them, P l·i,s,t Let P be the transmission power of the i-th tie line at time t in the s-th scenario. l·i,s,t A negative value indicates that the lower-level power grid feeds back to the upper-level power grid, P l·i,min P is the minimum power transmitted by the i-th tie line. l·i,max These represent the maximum transmission power of the i-th tie line.

[0084] For example, the tie-line power backfeed probability constraint can be expressed as the following formula.

[0085] Pr{P l·i,s,t ≤0}≤δ;

[0086] Among them, P l·i,s,t Let Pr{P} be the transmission power of the i-th tie line at time t in the s-th scenario. l·i,s,t ≤0} is P l·i,s,t The probability is ≤0, and δ is the probability of power backfeed.

[0087] For example, the capacity constraint for new energy access at each node can be expressed as the following formula.

[0088]

[0089] in, The renewable energy access capacity of the i-th node in the upper-level power grid; These represent the renewable energy access capacity of the j-th node in the lower-level power grid; This represents the maximum renewable energy access capacity of the i-th node in the upstream power grid. These represent the maximum new energy access capacity of the i-th node in the lower-level power grid.

[0090] It should be noted that the evaluation device needs to partition and decouple the two-level distribution network in the region.

[0091] Based on the differences in power grid structure and operation, the zoning results are obtained according to different voltage levels and electrically independent standards, that is, each distribution network in the two-level distribution network is an independent sub-region.

[0092] The coupling relationship between two-level distribution networks in a region is reflected in the power interaction of tie lines. Decoupling of two-level distribution networks in a region involves copying the nodes at both ends of the tie line to the adjacent sub-regions respectively, establishing the power connection of the boundary branches of the adjacent sub-regions. After decoupling, the power direction and magnitude of the boundary branches of the two-level distribution networks should be the same.

[0093] For example, the evaluation device can achieve partitioning and decoupling in the following manner.

[0094]

[0095] in, Let be the power transmitted from the upper-level power grid to the nth lower-level power grid at time t in the s-th scenario; Let be the power transmitted from the nth lower-level power grid to the upper-level power grid at time t in the s-th scenario.

[0096] It should be noted that the evaluation device can decompose multi-level distribution networks using Lagrange functions.

[0097] The Lagrange function is described below;

[0098]

[0099] in, For a multi-level distribution network, the Lagrangian function is... Let be the optimization variable in the upper-level power grid of the s-th scenario; Let λ be the optimization variable in the lower-level power grid of the s-th scenario; s Let λ be the set of Lagrange multipliers formed by the n connecting lines of the s-th scene; n,s ρ is the Lagrange multiplier for the iteration between the nth connection line in the s-th scene; n,s Let M be the penalty coefficient for iterations between the nth connection line in the s-th scene; M is the total number of time points in each scene. Let be the photovoltaic access capacity at the upstream grid node i of the s-th scenario; Let be the wind power access capacity at the i-th upstream grid node of the s-th scenario; Let be the photovoltaic grid connection capacity at node j of the lower-level grid in the s-th scenario. Let be the power transmitted from the upper-level power grid to the nth lower-level power grid at time t in the s-th scenario; Let N1 be the power transmitted from the nth lower-level power grid to the upper-level power grid at time t in the s-th scenario; N1 is the number of nodes in the upper-level power grid. 2,n N1 represents the number of nodes in the nth lower-level power grid; N2 represents the number of lower-level power grids connected to the upper-level power grid.

[0100] For example, the objective function of the two-level distribution network access capacity sub-problem can be expressed as the following formula.

[0101]

[0102]

[0103] in, For the Lagrangian function of the upper-level distribution network, It is the Lagrange function of the lower-level distribution network.

[0104] It should be noted that the evaluation device can determine the optimal solution of the objective function based on a variety of evaluation strategies.

[0105] Optionally, the evaluation strategy may include a sub-regional independent optimization strategy and a sub-regional collaborative optimization strategy.

[0106] For example, the sub-region independent optimization strategy only considers the optimization of its own renewable energy access volume, without considering the renewable energy configuration and operation of other sub-regions. Under the premise of ensuring that all constraints are met, the renewable energy access capacity of each node is changed to maximize the access capacity of each sub-region, and the power transmission value of the tie line is calculated at the same time.

[0107] For example, the inter-regional collaborative optimization strategy uses tie lines to transmit surplus power in sub-regions of the distribution network, addressing situations where a single-level distribution network cannot absorb renewable energy. Based on the self-optimization of each sub-region's distribution network, N sets of alternating iterative optimizations are performed on the upper-level grid and N lower-level grids to determine the direction and magnitude of power transmission on the tie lines, and to derive the maximum access capacity under each scenario. Finally, the minimum value among the results of each scenario represents the maximum renewable energy carrying capacity of the two-level distribution network in the region.

[0108] It should be noted that the optimal solution of the objective function can be determined using the alternating direction multiplier method. The iterative formula for the alternating direction multiplier method can be expressed as follows.

[0109]

[0110]

[0111]

[0112] Where k is the number of iterations.

[0113] It should be noted that this invention uses a two-level regional distribution network as an example for illustration. The assessment process of the renewable energy carrying capacity of a multi-level distribution network includes zoning, decoupling, problem decomposition, and solution.

[0114] For example, the partitioning and decoupling process may include steps A1-A4.

[0115] A1, such as Figure 6 As shown, based on the differences in power grid structure and operation, the two-level distribution network in the region is divided into zones according to different voltage levels and electrically independent standards, that is, each distribution network in each level of the power grid is an independent sub-region.

[0116] A2. Based on the partitioning results obtained from the above steps, decouple the connection lines between adjacent sub-regions that have a coupling relationship, such as... Figure 7 As shown, the nodes at both ends of the connector are copied to the adjacent sub-regions, and then... Establish power connections between boundary branches of distribution networks at all levels.

[0117] A3. Based on the partitioning and decoupling results of the above steps, the evaluation model is transformed into a distributed optimization model based on the principle of alternating direction multiplier method. The problem of new energy access capacity of regional two-level distribution network is decomposed into the sub-problem of new energy access capacity of each sub-regional distribution network, and the distributed optimization model is solved by alternating direction multiplier algorithm.

[0118] A4. Based on the power relationship of the boundary branches Introducing it into the objective function yields the Lagrange function.

[0119] In some embodiments, the objective function of the sub-optimization problem of the upper and lower level distribution networks can be obtained from the Lagrangian function. The distributed optimization process of the objective function is as follows: Figure 6 As shown, according to and Optimize the variables of the upper and lower level sub-problems in turn. and Optimization is performed, and the tie-line power is transferred between coupled sub-regions. The optimization result is the optimal value obtained in the k-th iteration of the alternating direction multiplier method for each subproblem. Then, by... Update the Lagrange multipliers λs as parameters for the (k+1)th iteration.

[0120] It is understandable that after updating the variable, according to The convergence of the algorithm is determined by calculating the average difference of the connection lines between adjacent sub-regions at each time step. The iteration ends when the average difference is not greater than the convergence accuracy ε; otherwise, the above steps are repeated.

[0121] As one possible implementation, the evaluation device can determine the optimal solution of the objective function through steps 31-34 based on a sub-region independent optimization strategy.

[0122] Step 31: Based on the safe operation conditions of the multi-level distribution network, randomly determine the access capacity of new energy sources at each node in the multi-level distribution network.

[0123] Step 32: Input the randomly determined access capacity of new energy sources at each node into the objective function to calculate the access capacity of new energy sources in each sub-region of the multi-level distribution network.

[0124] Step 33: Based on the safe operation conditions of the multi-level distribution network, change the access capacity of new energy sources at each node in the multi-level distribution network, repeat step 32, and obtain the access capacity of new energy sources in each sub-region of the multi-level distribution network calculated through multiple iterations.

[0125] Step 34: Sum the maximum access capacity of new energy sources in each sub-region to obtain the maximum access capacity of new energy sources in the multi-level distribution network.

[0126] The optimal solution is the new energy access capacity of each node corresponding to the maximum access capacity of new energy in the multi-level distribution network.

[0127] As another possible implementation, the evaluation device can also determine the optimal solution of the objective function through steps one to five by adopting the alternating direction multiplier method based on the interval collaborative optimization strategy.

[0128] Step 1: Initialize the distribution network parameters and the alternating direction multiplier method parameters.

[0129] As one possible implementation, the evaluation device can randomly determine the access capacity of new energy sources at each node of the upper-level distribution network in the multi-level distribution network based on the safe operating conditions of the multi-level distribution network.

[0130] As one possible implementation, the evaluation device can determine k=1 and determine the alternating direction multiplier method parameters based on the following formula.

[0131]

[0132]

[0133]

[0134] Where k is the number of iterations.

[0135] Step 2: Solve the optimization sub-problem of the upper-level distribution network; and calculate the transmission power of the tie line after decoupling the upper and lower-level distribution networks.

[0136] As one possible implementation, the evaluation device can input the access capacity of new energy sources at each node into the objective function to calculate the access capacity of new energy sources in the upper-level distribution network in the multi-level distribution network; change the access capacity of new energy sources at each node of the upper-level distribution network, repeat step 42, and determine the maximum access capacity of new energy sources in the upper-level distribution network in the multi-level distribution network.

[0137] As one possible implementation, the evaluation device can also determine the transmission power of the tie line between the upper-level and lower-level distribution networks based on the maximum access capacity of the upper-level distribution network's new energy sources.

[0138] Step 3: Based on the transmission power of the tie lines after decoupling of the upper and lower level distribution networks, solve the optimization sub-problems of each lower level distribution network.

[0139] As one possible implementation, the evaluation device can randomly determine the renewable energy access capacity of each node in the lower-level distribution network based on the transmission power of the tie line between the upper-level and lower-level distribution networks and the safe operating conditions of the lower-level distribution network. The renewable energy access capacity of each node in the lower-level distribution network is input into the objective function to calculate the renewable energy access capacity of each sub-region of the lower-level distribution network. By changing the renewable energy access capacity of each node in the lower-level distribution network, step 46 is repeated to determine the maximum renewable energy access capacity of the lower-level distribution network.

[0140] Step 4: Update the Lagrange multipliers.

[0141] As one possible implementation, the evaluation device can increment the value of k by 1 and update the Lagrange multipliers based on the following formula.

[0142]

[0143]

[0144]

[0145] Where k is the number of iterations.

[0146] Step 5: Determine whether the convergence accuracy has reached the set accuracy. If it has, exit the iteration process and output the optimal solution. If it has not reached the set accuracy, repeat steps 2 to 5 until the convergence accuracy reaches the set accuracy.

[0147] As one possible implementation, the evaluation device can determine the maximum access capacity of new energy in a multi-level distribution network based on the maximum access capacity of new energy in the upper-level distribution network and the maximum access capacity of new energy in the lower-level distribution network.

[0148] The optimal solution is the new energy access capacity of each node corresponding to the maximum access capacity of new energy in the multi-level distribution network.

[0149] In this way, this application can determine the maximum access capacity of new energy sources in a multi-level distribution network based on the alternating multiplier method, fully considering the mutual influence between upper and lower level distribution networks as well as the influence between distribution networks in each sub-region, thereby improving the rationality of the maximum access capacity of new energy sources in a multi-level distribution network.

[0150] As another possible implementation, the evaluation device can be based on the particle swarm optimization algorithm to determine the optimal solution of the objective function.

[0151] Step 51: Initialize the particle swarm algorithm parameters and the new energy access capacity of each node.

[0152] Step 52: Initialize the number of iterations to 1.

[0153] Step 53: Input the new energy access capacity of each node into the objective function to calculate the new energy access capacity of the multi-level distribution network.

[0154] Step 54: Determine the global optimal solution. The global optimal solution is the new energy access capacity of each node corresponding to the maximum access capacity of new energy in the multi-level distribution network.

[0155] Step 55: Determine if the number of iterations is greater than or equal to the set number. If yes, output the global optimal solution and exit the iteration process. If no, increment the number of iterations by 1, update the new energy access capacity of each node, and repeat steps 53-55 until exiting the iteration process.

[0156] S104. Determine the minimum value among the maximum access capacities corresponding to each cluster scenario as the maximum access capacity of new energy in the multi-level distribution network, and evaluate the new energy carrying capacity of the multi-level distribution network based on the new energy access capacity of each node corresponding to this minimum value.

[0157] For example, such as Figure 8 As shown, taking a regional two-level distribution network model as an example, the voltage deviation limits for the two levels of the distribution network are set to ±5% and ±7%, respectively; the upper and lower limits of tie-line transmission power are set to -5MW and 40MW, respectively, and the power backfeed probability is taken as 0.1. The limit of renewable energy access capacity at each node is 2MW; the initial total renewable energy access capacity at each level of the grid is 4MW and 5MW, respectively; the power factor of renewable energy units is set to 0.75 and is regarded as PQ nodes. The iteration parameter ρ of the ADMM algorithm is 0.1, the Lagrange multiplier is 1, the initial value of tie-line power is 0, and the original residual accuracy is 0.01.

[0158] like Figure 9 As shown, the maximum renewable energy carrying capacity of the distribution network in this area is 40.32MW. Figure 10 As shown, taking scenario 1 as an example, the maximum access capacity of new energy sources in each level of the distribution network in this area is solved using the alternating direction multiplier method. The maximum access capacity of new energy sources in each level of the distribution network gradually increases from the initial total access capacity and converges after 38 iterations. Figure 11 As shown, the power backflow phenomenon only occurs during the period from 12:00 to 15:00. Each sub-regional distribution network prioritizes the consumption of its own new energy sources, and at the same time, power exchange is carried out through tie lines during each time period. By transferring the surplus power of new energy sources, it assists adjacent distribution networks in meeting load demands, thereby achieving maximum access and coordinated consumption of new energy sources.

[0159] To verify the effectiveness of the collaborative evaluation strategy for the renewable energy carrying capacity of the regional distribution network, three evaluation strategies were set up, and the maximum renewable energy access capacity and optimization time for each scenario were compared and analyzed. Evaluation Strategy 1: A centralized evaluation model for renewable energy carrying capacity, using the particle swarm optimization algorithm to optimize the renewable energy carrying capacity of the two-level regional distribution network, without considering the backfeeding of tie-line power; Evaluation Strategy 12: Based on Model 1, considering the backfeeding of tie-line power; Evaluation Strategy 13: The distributed evaluation model for renewable energy carrying capacity proposed in this paper, using the ADMM algorithm to perform distributed collaborative optimization of the renewable energy carrying capacity of the two-level regional distribution network, considering the backfeeding of tie-line power.

[0160] like Figure 12 As shown, in each scenario, the maximum renewable energy access capacity of the regional distribution network considering backfeed power from tie lines is higher than that not considering backfeed. For example... Figure 13 As shown, Model 3 has a longer optimization time than Model 2. The dimensionality of optimization variables in each sub-optimization problem is reduced compared to centralized optimization. However, distributed evaluation requires continuous interaction of tie-line power information between sub-problems until iterative convergence, which increases the optimization time.

[0161] This invention provides a method for assessing the renewable energy carrying capacity of a multi-level distribution network. By performing cluster analysis on historical renewable energy and load data, various scenarios characterizing renewable energy generation are obtained. Within each cluster scenario, the maximum access capacity of the multi-level distribution network is calculated. Finally, the minimum value among the maximum access capacities corresponding to each cluster scenario is determined as the maximum renewable energy access capacity of the multi-level distribution network. Using the minimum value among all cluster scenarios to assess the maximum renewable energy carrying capacity of the multi-level distribution network avoids wind and solar curtailment due to excess renewable energy generation in different scenarios or time periods. This invention improves the rationality of determining the maximum renewable energy access capacity and can reasonably assess the renewable energy carrying capacity of a multi-level distribution network.

[0162] Optionally, the method for assessing the renewable energy carrying capacity of a multi-level distribution network provided in this embodiment of the invention further includes:

[0163] S201. Obtain the actual installed capacity of new energy sources at each node in a multi-level distribution network.

[0164] S202. Based on the actual installed capacity of new energy sources at each node and the new energy access capacity at each node, formulate guiding measures for new energy sources in multi-level distribution networks.

[0165] The guidelines include the installed capacity of new energy sources at each node.

[0166] In this way, embodiments of the present invention can formulate guidance measures for new energy based on the actual installed capacity of new energy at each node and the new energy access capacity at each node, thereby optimizing the installed capacity of new energy in the distribution network.

[0167] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0168] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0169] Figure 14 This diagram illustrates the structure of an assessment device for the renewable energy carrying capacity of a multi-level distribution network according to an embodiment of the present invention. The assessment device 300 includes a communication module 301 and a processing module 302.

[0170] The communication module 301 is used to acquire renewable energy data and load data during historical periods. The renewable energy data includes wind speed data and / or solar radiation data.

[0171] The processing module 402 is used to perform clustering processing on new energy data and load data to determine multiple clustering scenarios. The clustering scenarios are used to characterize the power generation characteristics of new energy power generation. For each clustering scenario, the optimal solution of the objective function is calculated with the maximum access capacity of new energy in the multi-level distribution network as the objective function and the safe operation conditions of the multi-level distribution network as the constraint. The optimal solution includes the maximum access capacity of the multi-level distribution network and the new energy access capacity of each node. The minimum value among the maximum access capacities corresponding to each clustering scenario is determined as the maximum access capacity of new energy in the multi-level distribution network, and the new energy carrying capacity of the multi-level distribution network is evaluated based on the new energy access capacity of each node corresponding to this minimum value.

[0172] In one possible implementation, the processing module 402 is specifically used to perform the following steps: Step 21: Normalize the new energy data and load data to obtain normalized data; Step 22: Based on the normalized data, randomly select K days of data as K cluster centers; where K is a positive integer; Step 23: Use the K-means clustering algorithm to calculate the Euclidean distance from the data at each time point in the normalized data to the K cluster centers; and sum the multiple Euclidean distances to obtain the total Euclidean distance; Step 24: Change the position of the K cluster centers, repeat step 23, compare the total Euclidean distances obtained multiple times, and select the K days of data corresponding to the minimum value among the multiple total Euclidean distances as the cluster data; Step 25: Calculate the clustering effectiveness index based on the cluster data; Step 26: Change the value of K, repeat steps 22-25, obtain the clustering effectiveness index obtained multiple times, and select the K days corresponding to the maximum value among the multiple clustering effectiveness indices as the K clustering scenarios.

[0173] In one possible implementation, the processing module 402 is specifically used to determine the clustering effectiveness index based on the following formula;

[0174]

[0175] Where M represents the number of clusters, m represents the current class, and v i Indicates the class center, x represents the center point of the dataset. i Representing each point in the class, Tr(S) B ) represents the trace of the between-class deviation matrix, used to measure the separation of the dataset, Tr(S W ) represents the trace of the intra-class deviation matrix, used to measure the density of data within a class.

[0176] In one possible implementation, the processing module 402 is specifically used to perform the following steps: Step 31: Based on the safe operation conditions of the multi-level distribution network, randomly determine the access capacity of new energy sources at each node in the multi-level distribution network; Step 32: Input the randomly determined access capacity of new energy sources at each node into the objective function to calculate the access capacity of new energy sources in each sub-region of the multi-level distribution network; Step 33: Based on the safe operation conditions of the multi-level distribution network, change the access capacity of new energy sources at each node in the multi-level distribution network, repeat Step 32, and obtain the access capacity of new energy sources in each sub-region of the multi-level distribution network calculated through multiple iterations; Step 34: Sum the maximum values ​​of the access capacity of new energy sources in each sub-region to obtain the maximum access capacity of new energy sources in the multi-level distribution network, and the optimal solution is the new energy access capacity of each node corresponding to the maximum access capacity of new energy sources in the multi-level distribution network.

[0177] In one possible implementation, the processing module 402 is specifically used to perform the following steps: Step 1, initialize the distribution network parameters and the alternating direction multiplier method parameters; Step 2, solve the optimization subproblem of the upper-level distribution network and calculate the transmission power of the tie line after decoupling between the upper and lower-level distribution networks; Step 3, based on the transmission power of the tie line after decoupling between the upper and lower-level distribution networks, solve the optimization subproblem of each lower-level distribution network; Step 4, update the Lagrange multipliers; Step 5, determine whether the convergence accuracy has reached the set accuracy; if the set accuracy has been reached, exit the iteration process and output the optimal solution; if the set accuracy has not been reached, repeat steps 2 to 5 until the convergence accuracy reaches the set accuracy.

[0178] In one possible implementation, the processing module 402 is specifically used to perform the following steps: Step 51, initialize the particle swarm optimization algorithm parameters and the renewable energy access capacity of each node; Step 52, initialize the iteration count to 1; Step 53, input the renewable energy access capacity of each node into the objective function to calculate the renewable energy access capacity of the multi-level distribution network; Step 54, determine the global optimal solution, which is the renewable energy access capacity of each node corresponding to the maximum renewable energy access capacity of the multi-level distribution network; Step 55, determine whether the iteration count is greater than or equal to the set number. If yes, output the global optimal solution and exit the iteration process; if no, increment the iteration count by 1, update the renewable energy access capacity of each node, and repeat steps 53-55 until exiting the iteration process.

[0179] In one possible implementation, the communication module 401 is further used to obtain the actual installed capacity of new energy sources at each node in the multi-level distribution network; the processing module 402 is further used to formulate guidance measures for new energy sources in the multi-level distribution network based on the actual installed capacity of new energy sources at each node and the new energy access capacity at each node, the guidance measures including the pending installed capacity of new energy sources at each node.

[0180] Figure 15This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 15 As shown, the electronic device 400 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the above-described method embodiments, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 14 The functions of the communication module 301 and the processing module 302 shown are illustrated.

[0181] For example, the computer program 403 can be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 403 in the electronic device 400. For example, the computer program 403 can be divided into... Figure 14 The communication module 301 and the processing module 302 are shown.

[0182] The processor 401 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0183] The memory 402 can be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. The memory 402 can also be an external storage device of the electronic device 400, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 400. Furthermore, the memory 402 can include both internal and external storage units of the electronic device 400. The memory 402 is used to store the computer program and other programs and data required by the terminal. The memory 402 can also be used to temporarily store data that has been output or will be output.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0185] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0187] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0190] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0191] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for evaluating the renewable energy carrying capacity of a multi-level distribution network, characterized in that, include: Acquire historical renewable energy data and load data, including wind speed data and / or solar radiation data; perform clustering processing on the renewable energy data and load data to determine multiple clustering scenarios; wherein, the clustering scenarios are used to characterize the power generation characteristics of renewable energy generation; For each clustering scenario, the objective function is to maximize the access capacity of new energy sources in the multi-level distribution network, and the constraint is the safe operation condition of the multi-level distribution network. The optimal solution of the objective function is calculated, including: Step 1, initializing the distribution network parameters and the alternating direction multiplier method parameters; Step 2, solving the optimization sub-problem of the upper-level distribution network and calculating the transmission power of the tie lines after decoupling the upper and lower-level distribution networks; Step 3, based on the transmission power of the tie lines after decoupling the upper and lower-level distribution networks, solving the optimization sub-problem of each lower-level distribution network; Step 4, updating the Lagrange multipliers; Step 5, determining whether the convergence accuracy has reached the set accuracy; if the set accuracy has been reached, the iteration process is exited and the optimal solution is output; if the set accuracy has not been reached, steps 2 to 5 are repeated until the convergence accuracy reaches the set accuracy; the optimal solution includes the maximum access capacity of the multi-level distribution network and the new energy access capacity of each node. The minimum value among the maximum access capacities corresponding to each cluster scenario is determined as the maximum access capacity of new energy in the multi-level distribution network. Based on the new energy access capacity of each node corresponding to this minimum value, the new energy carrying capacity of the multi-level distribution network is evaluated.

2. The method for evaluating the renewable energy carrying capacity of a multi-level distribution network according to claim 1, characterized in that, The clustering process performed on the new energy data and load data identifies multiple clustering scenarios, including: Step 21: Normalize the new energy data and load data to obtain normalized data; Step 22: Based on the normalized data, randomly select K days of data as K cluster centers; where K is a positive integer; Step 23: Use the K-means clustering algorithm to calculate the Euclidean distance from the data at each time point in the normalized data to the K cluster centers; and sum the multiple Euclidean distances to obtain the total Euclidean distance; Step 24: Change the positions of the K cluster centers, repeat step 23, compare the total Euclidean distances calculated multiple times, and select the data of the K days corresponding to the minimum value among the multiple total Euclidean distances as the cluster data; Step 25: Calculate the clustering effectiveness index based on the clustering data; Step 26: Change the K value and repeat steps 22-25 to obtain the clustering effectiveness index calculated multiple times. Select the K days corresponding to the maximum value among the multiple clustering effectiveness indices as the K clustering scenarios.

3. The method for evaluating the renewable energy carrying capacity of a multi-level distribution network according to claim 1, characterized in that, The calculation of the clustering effectiveness index based on the clustering data includes: The clustering effectiveness index is determined based on the following formula; ； Where M represents the number of clusters, and m represents the current cluster. Indicates the class center, Represents the center point of the dataset. Representing points in the class, The trace represents the inter-class deviation matrix and is used to measure the separation of the dataset. The trace represents the intra-class deviation matrix and is used to measure the density of data within a class.

4. The method for evaluating the renewable energy carrying capacity of a multi-level distribution network according to claim 1, characterized in that, The method further includes: Obtain the actual installed capacity of new energy sources at each node in a multi-level distribution network; Based on the actual installed capacity of new energy sources at each node and the new energy access capacity at each node, guidance measures for new energy sources in multi-level distribution networks are formulated, including the pending installed capacity of new energy sources at each node.

5. An evaluation device for the renewable energy carrying capacity of a multi-level distribution network, characterized in that, include: The communication module is used to acquire new energy data and load data during historical periods, wherein the new energy data includes wind speed data and / or solar radiation data; The processing module is used to perform clustering processing on the new energy data and load data to determine multiple clustering scenarios. These clustering scenarios characterize the power generation features of new energy generation. For each clustering scenario, the optimal solution of the objective function is calculated, with the maximum access capacity of the multi-level distribution network as the objective function and the safe operation conditions of the multi-level distribution network as the constraint. The optimal solution includes the maximum access capacity of the multi-level distribution network and the new energy access capacity of each node. The minimum value among the maximum access capacities corresponding to each clustering scenario is determined as the maximum access capacity of the multi-level distribution network for new energy. Based on the new energy access capacity of each node corresponding to this minimum value, the new energy carrying capacity of the multi-level distribution network is evaluated. The processing module is specifically used for: Step 1, initializing the distribution network parameters and the alternating direction multiplier method parameters; Step 2, solving the optimization sub-problem of the upper-level distribution network and calculating the transmission power of the tie lines after decoupling the upper and lower-level distribution networks; Step 3, solving the optimization sub-problems of each lower-level distribution network based on the transmission power of the tie lines after decoupling the upper and lower-level distribution networks; Step 4, updating the Lagrange multipliers; Step 5, determining whether the convergence accuracy has reached the set accuracy; if the set accuracy has been reached, exiting the iteration process and outputting the optimal solution; if the set accuracy has not been reached, repeating steps 2 to 5 until the convergence accuracy reaches the set accuracy.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4 above.

Images