Distributed resource model flexibility aggregation method and device and computer equipment

Abstract

The invention relates to a distributed resource model flexibility aggregation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of distributed resource models; the feasible regions of the multiple distributed resource models are converted into compact expressions; calculating a reference coefficient according to all coefficient matrixes in the compact expression; substituting the reference coefficient into the compact expression to obtain a reference expression; taking internal approximation of an original feasible region of the distributed resource model as a target to adjust the reference expression, and obtaining an internal approximation feasible region; calculating the similarity between the original feasible regions; clustering the original feasible regions based on similarity to obtain a plurality of original feasible region sets; obtaining a plurality of inner approximate feasible region sets corresponding to the plurality of feasible region sets; and aggregating the plurality of inner approximate feasible region sets to obtain an aggregation model of the plurality of distributed resource models. By adopting the method, the flexible aggregation of the distributed resource model can be realized.

Description

Technical Field

[0001] This application relates to the technical field, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for flexible aggregation of distributed resource models. Background Technology

[0002] With the continuous proposal and advancement of new power systems, the proportion of installed capacity from new energy sources is constantly increasing. However, the diverse types and heterogeneous characteristics of flexible resources pose significant challenges to aggregated modeling and dispatch control.

[0003] Most existing distributed resource flexibility aggregation methods rely on the numerical characteristics of parameter vectors (such as rated power, capacity, charge / discharge rate, etc.) for clustering or aggregation. They construct the aggregation feasible region solely based on numerical similarity, ignoring the differences in operational constraints, temporal coupling relationships, and flexibility boundary morphology among different distributed resource technologies (such as energy storage temperature-controlled loads, electric vehicles, etc.). However, this method struggles to accurately characterize the true aggregation flexibility region in scenarios with strong distributed resource heterogeneity, and may even lead to overly conservative aggregation models, resulting in the overall system flexibility potential not being fully realized, and potentially impacting the feasibility and economy of power system dispatch.

[0004] Therefore, how to comprehensively consider the similarities in the characteristics of distributed resource technologies is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer device, computer-readable storage medium, and computer program product that can comprehensively consider the similarity of distributed resource technology characteristics to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for aggregating the flexibility of a distributed resource model, including:

[0007] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0008] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0009] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0010] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0011] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0012] In one embodiment, multiple feasible domains of distributed resource models are transformed into compact expressions, including:

[0013] Based on multiple distributed resource models, obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models;

[0014] Based on the coefficient matrix and parameter vector, construct compact expressions for multiple distributed resource models.

[0015] In one embodiment, the baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model to obtain the inner approximate feasible regions of multiple distributed resource models, including:

[0016] Construct an objective function with the goal of maximizing the scaling factor, and obtain the constraints of the objective function based on a compact expression;

[0017] Under the constraints, the objective function is solved to obtain a scaling factor that approximates the original feasible region of multiple distributed resource models.

[0018] By adjusting the baseline expression using a scaling factor, the inner approximate feasible region of multiple distributed resource models is obtained.

[0019] In one embodiment, calculating the similarity between the inner approximate feasible regions of multiple distributed resource models includes:

[0020] Based on the coefficient matrix of the compact expression, calculate the directional similarity between the feasible regions of multiple distributed resource models;

[0021] Based on the parameter vector of the compact expression, the distance similarity between the feasible regions of multiple distributed resource models is calculated.

[0022] A similarity matrix is ​​constructed based on directional similarity and distance similarity; the similarity matrix is ​​used as the similarity between feasible regions.

[0023] In one embodiment, based on the similarity, the original feasible domains are clustered to obtain multiple sets of original feasible domains, including:

[0024] Based on similarity, a pre-defined method is used to cluster the feasible region, and pre-defined coefficients are calculated; the pre-defined coefficients are used to evaluate the clustering results.

[0025] The optimal number of clusters is determined based on preset coefficients;

[0026] Based on the optimal number of clusters and the clustering results, multiple original feasible domain sets are obtained.

[0027] In one embodiment, multiple sets of internal approximate feasible domains are aggregated to obtain an aggregated model of multiple distributed resource models, including:

[0028] By using a preset summation method, compact expressions from multiple sets of internal approximate feasible regions are aggregated to obtain an aggregated model of multiple distributed resource models.

[0029] Secondly, this application also provides a distributed resource feasible domain aggregation device, comprising:

[0030] The transformation module is used to obtain multiple distributed resource models and transform the feasible domains of the multiple distributed resource models into compact expressions.

[0031] The calculation module is used to calculate the baseline coefficients based on all the coefficient matrices in the compact expression; and to substitute the baseline coefficients into the compact expression to obtain the baseline expression.

[0032] The adjustment module is used to adjust the baseline expression with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and to obtain the inner approximate feasible regions of multiple distributed resource models.

[0033] The clustering module is used to calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, the original feasible domains are clustered to obtain multiple sets of original feasible domains.

[0034] The aggregation module is used to obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; and to aggregate multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0037] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0038] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0039] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0040] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0043] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0044] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0045] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0046] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0049] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0050] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0051] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0052] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0053] The aforementioned method, apparatus, computer device, computer-readable storage medium, and computer program product for aggregating feasible regions of distributed resources acquire multiple distributed resource models; transform the feasible regions of the multiple distributed resource models into compact expressions; calculate baseline coefficients based on all coefficient matrices in the compact expressions; substitute the baseline coefficients into the compact expressions to obtain a baseline expression; adjust the baseline expression with the goal of constructing an inner approximation of the original feasible regions of the distributed resource models to obtain inner approximate feasible regions of the multiple distributed resource models; calculate the similarity between the original feasible regions of the multiple distributed resource models; cluster the original feasible regions based on the similarity to obtain multiple sets of original feasible regions; obtain multiple sets of inner approximate feasible regions corresponding to the multiple sets of feasible regions; and aggregate the multiple sets of inner approximate feasible regions to obtain an aggregated model of the multiple distributed resource models. By constructing a compact expression, the complex feasible region problem is transformed into a mathematical optimization problem. Then, by solving the objective function, the inner approximate feasible region is obtained. At the same time, the similarity-based clustering method can ensure that the distributed resource models in the same class have high technical similarity, so that they can better maintain their respective flexibility characteristics during aggregation. This can adapt to the distributed resource aggregation needs in different scenarios and provide strong technical support for the stable operation and efficient scheduling of new power systems. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is an application environment diagram of the distributed resource model flexibility aggregation method in one embodiment;

[0056] Figure 2 This is a flowchart illustrating a distributed resource model flexibility aggregation method in one embodiment;

[0057] Figure 3 This is a schematic diagram of a two-dimensional planar abstract feasible region of distributed resources in one embodiment;

[0058] Figure 4 Here is an example diagram of the Minkowski summation method in one embodiment;

[0059] Figure 5This is a flowchart illustrating the distributed resource model flexibility aggregation method in another embodiment;

[0060] Figure 6 This is a graphical illustration of approximate flexible aggregation in one embodiment;

[0061] Figure 7 This is a comparison chart of clustering effects in one embodiment;

[0062] Figure 8 This is a comparison diagram of the convex polyhedron volume under different numbers of distributed energy sources in one embodiment;

[0063] Figure 9 This is a structural block diagram of a distributed resource model flexibility aggregation device in one embodiment;

[0064] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0067] The distributed resource model flexibility aggregation method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on a cloud or other network server. Specifically, terminal 102 or server 104 implements a distributed resource model flexibility aggregation method, which includes:

[0068] The process involves: acquiring multiple distributed resource models; transforming the feasible regions of these models into compact expressions; calculating baseline coefficients based on all coefficient matrices in the compact expressions; substituting the baseline coefficients into the compact expressions to obtain the baseline expressions; adjusting the baseline expressions with the goal of constructing an inner approximation of the original feasible regions of the distributed resource models to obtain the inner approximation feasible regions of the multiple distributed resource models; calculating the similarity between the original feasible regions of the multiple distributed resource models; clustering the original feasible regions based on the similarity to obtain multiple sets of original feasible regions; obtaining multiple sets of inner approximation feasible regions corresponding to the multiple sets of feasible regions; and aggregating the multiple sets of inner approximation feasible regions to obtain an aggregated model of the multiple distributed resource models.

[0069] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0070] In one exemplary embodiment, such as Figure 2 As shown, a flexible aggregation method for distributed resource models is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 210. Wherein:

[0071] Step 202: Obtain multiple distributed resource models; transform the feasible domains of the multiple distributed resource models into compact expressions.

[0072] Among these, flexibility aggregation maps the technical constraint space of each distributed energy source to the aggregated power constraint space; the distributed resource model refers to transforming individual distributed energy entities into variables and constraints that optimization algorithms can understand and process, such as energy storage models, temperature-controlled load models, and electric vehicle models. The compact expression includes a coefficient matrix and a parameter vector.

[0073] For example, multiple distributed energy sources are modeled to obtain multiple distributed resource models. After modeling the distributed resources, the feasible region consisting of their technical constraints is obtained. The feasible region should be a high-dimensional polyhedron.

[0074] Optionally, the technical characteristics of each distributed resource model are analyzed to obtain the coefficient matrix and parameter vector of each distributed resource model, and a compact expression is obtained based on the coefficient matrix and parameter vector.

[0075] Alternatively, the compact expression is:

[0076]

[0077] in, For the first The coefficient matrix of a distributed resource model For the first A parameter vector of a distributed resource model; For the first The constraint space of a distributed resource model. For the first The charging and discharging power of a distributed resource.

[0078] Step 204: Calculate the baseline coefficients based on all coefficient matrices in the compact expression; substitute the baseline coefficients into the compact expression to obtain the baseline expression.

[0079] For example, the benchmark coefficient is expressed as:

[0080]

[0081] Where N represents the number of distributed resource models.

[0082] Alternatively, substituting the benchmark coefficients into the compact expression yields the benchmark expression as follows:

[0083]

[0084] in, As the benchmark coefficient, As the baseline parameter, This serves as the benchmark for averaging.

[0085] Step 206: Adjust the baseline expression with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and obtain the inner approximation feasible regions of multiple distributed resource models.

[0086] The inner approximate feasible region represents a simple and easily computed mathematical set used to conservatively describe the real and complex flexibility feasible region of a distributed resource.

[0087] Optionally, the reference expression can be adjusted by means of displacement, scaling, etc.

[0088] Optionally, to make the inner approximate feasible region closer to the original feasible region, an objective function is set with maximizing the scaling factor as the goal, corresponding linear programming constraints are constructed, and the scaling factor and displacement factor of the distributed resources are solved. Based on the scaling factor and displacement factor, the baseline expression is adjusted to obtain the inner approximate feasible region. The inner approximate feasible region can be expressed as:

[0089]

[0090] Step 208: Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains.

[0091] Optionally, geometric similarity and distance similarity between feasible regions of multiple distributed resource models are calculated to construct a geometric-distance comprehensive similarity.

[0092] Optionally, based on the constructed geometric-distance comprehensive similarity, a hierarchical clustering algorithm is used to cluster the original feasible domains. First, each distributed resource model is regarded as an independent class, and then the distributed resource models with high similarity are divided into the same cluster to obtain multiple sets of original feasible domains.

[0093] Step 210: Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0094] If at least one original feasible domain of a distributed resource already exists, then the aggregation model is the Minkowski sum of these original feasible domains.

[0095] Optionally, after performing inner approximation and clustering on the original feasible region, for the obtained inner approximation feasible region set, the benchmarks within the cluster are the same, and the approximation feasible regions of individual flexible resources within the cluster are actually high-dimensional polyhedra with the same shape. Therefore, flexibility aggregation can be easily performed by Minkowski summation.

[0096]

[0097] In the formula, This represents the Minkowski summation operation. This is an aggregation model.

[0098] The aforementioned method for aggregating feasible regions of distributed resources involves: acquiring multiple distributed resource models; transforming the feasible regions of these models into compact expressions; calculating baseline coefficients based on all coefficient matrices within the compact expressions; substituting these baseline coefficients into the compact expressions to obtain the baseline expressions; adjusting the baseline expressions with the goal of constructing an inner approximation of the original feasible regions of the distributed resource models to obtain the inner approximation feasible regions of the multiple distributed resource models; calculating the similarity between the original feasible regions of the multiple distributed resource models; clustering the original feasible regions based on the similarity to obtain multiple sets of original feasible regions; obtaining multiple sets of inner approximation feasible regions corresponding to these sets; and aggregating these sets to obtain an aggregated model of the multiple distributed resource models. By constructing compact expressions, the complex feasible region problem is transformed into a mathematical optimization problem, and the inner approximation feasible region is obtained by solving the objective function. Furthermore, the similarity-based clustering method ensures that distributed resource models within the same class have high similarity in technical characteristics, thus better maintaining their flexibility during aggregation. This allows for adaptation to the aggregation needs of distributed resources in different scenarios, providing strong technical support for the stable operation and efficient dispatch of new power systems.

[0099] In one embodiment, transforming the feasible domain of multiple distributed resource models into a compact expression includes: obtaining the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; and constructing a compact expression for the multiple distributed resource models based on the coefficient matrix and parameter vector.

[0100] Optionally, the parameters are inherent physical or economic characteristics of the distributed resource. The coefficients are multiplier factors that constitute the mathematical constraints of the feasible region, defining the specific relationships between decision variables and between decision variables and parameters. Coefficients are generally represented in matrix form, i.e., a coefficient matrix.

[0101] In this embodiment, by accurately obtaining the coefficient matrix and parameter vector corresponding to the distributed resource model, a precise and compact expression can be constructed. This provides a solid and reliable foundation for a series of subsequent operations based on the compact expression, ensuring that the entire distributed resource model flexible aggregation process can be carried out based on accurate data and a reasonable mathematical model. This effectively improves the accuracy and reliability of the aggregation results and better meets the accuracy requirements of distributed resource aggregation for stable operation and efficient dispatch of the new power system.

[0102] In one embodiment, adjusting the baseline expression with the goal of constructing an inner approximation of the original feasible region of the distributed resource model to obtain the inner approximation feasible regions of multiple distributed resource models includes: constructing an objective function with the goal of maximizing the scaling factor, and obtaining the constraints of the objective function based on the compact expression; solving the objective function under the constraints to obtain the scaling factor with the goal of inner approximating the original feasible regions of multiple distributed resource models; and adjusting the baseline expression with the scaling factor to obtain the inner approximation feasible regions of multiple distributed resource models.

[0103] Alternatively, the objective function can be expressed as:

[0104]

[0105] Based on the coefficient matrix and parameter vector of the compact expression, the constraints of the objective function can be listed as follows:

[0106]

[0107]

[0108] in, ; ; For the first Scaling factor for a distributed resource model For the first The displacement factor of a distributed resource model.

[0109] In this embodiment, by constructing an objective function with the goal of maximizing the scaling factor and reasonably obtaining its constraints, the scaling factor is obtained by solving the objective function under the condition of satisfying the constraints. Then, the scaling factor is used to accurately adjust the baseline expression, which can make the inner approximate feasible region closer to the original feasible region. This helps to improve the accuracy and effectiveness of the flexibility aggregation of the entire distributed resource model, better adapt to the needs of distributed resource aggregation in different scenarios, and provide stronger support for the stable operation and efficient scheduling of new power systems.

[0110] In one embodiment, calculating the similarity between the inner approximate feasible regions of multiple distributed resource models includes: calculating the directional similarity between the feasible regions of multiple distributed resource models based on the coefficient matrix of the compact expression; calculating the distance similarity between the feasible regions of multiple distributed resource models based on the parameter vector of the compact expression; constructing a similarity matrix based on the directional similarity and the distance similarity; and using the similarity matrix as the similarity between the feasible regions.

[0111] For example, the technically feasible domain of a distributed resource can be transformed into a compact expression form, which can be illustrated using a two-dimensional plane as an example, where the technically feasible domain of a distributed resource is abstracted as follows: Figure 3The concave polyhedron shown.

[0112] In each half-space constraint, the row vectors of the coefficient matrix characterize the normal vector direction of the corresponding half-space in the feasible region, i.e., the slope of the hyperplane; the parameter vectors correspond to the intercept terms of each half-space, used to define the boundary position of the feasible region in that direction. Due to the strong heterogeneity among distributed resources, the technically feasible regions of different distributed resources differ in both the half-space normal vector and the intercept term. Distance similarity is used to measure the similarity of the intercept terms of each half-space.

[0113] Optionally, cosine similarity is used to quantify the geometric similarity of the half-space normal vectors. Cosine similarity is a commonly used method to quantify the similarity of vector directions. Its core idea is to reflect the degree of geometric similarity between two vectors by calculating the cosine of the angle between them. The calculation formula is as follows:

[0114]

[0115] This metric is independent of the magnitude of the vector and only reflects the consistency of direction, thus effectively eliminating the influence of scale differences.

[0116] For example, Chebyshev distance is used to represent the distance between parameter vectors of different individuals, and the specific formula is as follows:

[0117]

[0118] in, Represents the parameter vector the number of rows, For individuals parameter vector The Each element.

[0119] Using Pearson correlation matrix The specific formula for measuring the correlation between operating parameters of different individuals is as follows:

[0120]

[0121] in, For individuals parameter vector The average of all elements.

[0122] Distance similarity matrix The specific formula is as follows:

[0123]

[0124] in, This represents the distance similarity between the original feasible regions of a distributed resource model.

[0125] Optionally, a geometric-distance similarity matrix can be constructed by weighted fusion of geometric similarity and distance similarity. This matrix can comprehensively reflect the multidimensional similarity characteristics between feasible domains of different distributed energy technologies.

[0126] In this embodiment, by calculating directional similarity and distance similarity separately and constructing a geometric-distance similarity matrix, a comprehensive and accurate measure of the similarity between the inner approximate feasible regions of multiple distributed resource models is adopted. This ensures that distributed resource models in the same class have high similarity in technical characteristics, laying a solid foundation for the subsequent accurate construction of aggregation models. It helps to improve the rationality and effectiveness of the entire distributed resource model flexibility aggregation process, and better meet the accuracy requirements of distributed resource aggregation for stable operation and efficient dispatch of new power systems.

[0127] In one embodiment, clustering the original feasible domains based on the similarity to obtain multiple sets of original feasible domains includes: clustering the feasible domains based on the similarity using a preset method and calculating preset coefficients; using the preset coefficients to evaluate the clustering results; determining the optimal number of clusters based on the preset coefficients; and obtaining multiple sets of original feasible domains based on the optimal number of clusters and the clustering results.

[0128] The preset method is hierarchical clustering. This method is an unsupervised clustering algorithm based on a similarity matrix. It constructs a hierarchical clustering tree by progressively merging or splitting samples, thereby reflecting the similarity relationships between samples. The preset coefficient is the silhouette coefficient, which comprehensively considers the compactness of samples within clusters and the separation between clusters. The calculation formula is as follows:

[0129]

[0130] in, Indicates sample The average distance to other samples within the same cluster. Indicates sample The average distance to samples within the nearest neighbor cluster.

[0131] Furthermore, the profile coefficient The range of values ​​is The larger the value, the more reasonable the sample division. By calculating the average silhouette coefficient of all samples, the optimal number of clusters can be determined, thereby obtaining clustering results with high internal similarity and good inter-cluster discrimination.

[0132] For example, each distributed resource is first treated as an independent class, and then the clusters with the highest similarity are gradually merged based on comprehensive similarity until a complete clustering hierarchy is formed. Ultimately, distributed resources with high similarity will be grouped into the same cluster, achieving unified aggregation of resources with similar technical feasibility domains.

[0133] In this embodiment, by adopting hierarchical clustering and introducing the silhouette coefficient as a preset coefficient, the optimal number of clusters can be determined scientifically and reasonably, ensuring that the clustering results have both high internal similarity and good inter-cluster discrimination. This fully considers the differences in technical characteristics between distributed resource models and effectively avoids the problem of decreased accuracy of the aggregation model due to improper clustering.

[0134] In one embodiment, aggregating multiple sets of inner approximate feasible domains to obtain an aggregated model of multiple distributed resource models includes: aggregating compact expressions in multiple sets of inner approximate feasible domains using a preset summation method to obtain an aggregated model of multiple distributed resource models.

[0135] The default summation method is the Minkowski summation method, which involves geometric addition of multiple sets.

[0136] For example, the Minkowski sum is the sum of two Euclidean space point sets A and B, taking a two-time-phase scheduling problem of two flexible resources as an example. Assume the technical constraints of distributed resource model 1 and distributed resource model 2 are as follows:

[0137]

[0138] Minkowski summation for distributed resource model 1 and distributed resource model 2 is as follows: Figure 4 As shown in the diagram, when performing flexible aggregation across multiple time periods, large quantities, and various types, the problem of dimensionality curse will arise, making it difficult to accurately calculate the feasible region of flexibility.

[0139] In this embodiment, by using the Minkowski summation method to aggregate compact expressions in multiple inner approximate feasible domain sets, it is possible to effectively integrate the feasible domains of multiple distributed resource models at the mathematical level.

[0140] Next reference Figure 5 The distributed resource feasible domain aggregation method of this application will be illustrated by a most specific embodiment.

[0141] Step 1: Model the flexibility of distributed resources.

[0142] Next, we selected three typical resources—distributed energy storage, temperature-controlled loads, and electric vehicles—for modeling research. The proposed method has a certain degree of universality and is applicable to other flexible resource scenarios.

[0143] For the energy storage model, considering the charge / discharge and state of charge (SOC) boundaries, the technical constraints are as follows:

[0144] Power constraints:

[0145]

[0146]

[0147] Energy-power coupling constraints:

[0148]

[0149] Energy constraints:

[0150]

[0151] in, , Energy storage During the period The charging and discharging power; For energy storage Battery dissipation coefficient; For energy storage During the period Operating capacity; , For energy storage The charge / discharge efficiency coefficient; For energy storage Maximum charging and discharging power; , For energy storage Maximum and minimum capacity.

[0152] For the temperature control load model, taking HVAC as an example, the temperature control load is modeled as follows:

[0153] Power model:

[0154]

[0155]

[0156] Energy-power coupling constraints:

[0157]

[0158] Energy constraints:

[0159]

[0160] in, If the temperature control load is a state variable, then... In heating mode, If the value is 1, then it is in cooling mode. =-1; For temperature control load exist The indoor temperature at any given time; For temperature control load Factors affecting ambient temperature For temperature control load The difference between the ambient temperature and the user's preset temperature. , For temperature control load The minimum and maximum values ​​of the user-preset temperature.

[0161] For the electric vehicle model, the controllable time period of the electric vehicle is limited by its arrival and departure times at the station, and the owner's expected battery level must be met upon departure. The modeling for a single electric vehicle is as follows:

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] in, For electric vehicles Grid connection and off-grid times; , Electric vehicles exist The charging and discharging power at any given moment; For electric vehicles Initial capacity before grid connection; For electric vehicles Expected operating capacity when offline; , Electric vehicles The maximum value of charging and discharging power; , For electric vehicles exist The upper and lower limits of the runtime usage at any given time.

[0169] In practical applications, electric vehicles are typically connected to charging stations in clusters for unified management and control. Cluster management helps transform previously dispersed individual loads into schedulable and controllable flexible resources. The modeling of electric vehicle clusters is as follows:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] In an electric vehicle cluster, the grid connection and disconnection of individual electric vehicles can cause a jump in cluster capacity and power. Let electric vehicles... exist The grid connection status at any given time is , When =1, it indicates an electric vehicle. In grid-connected state, When =0, it indicates that the electric vehicle is in an off-grid state.

[0176] Step 2: Convert the flexibility resource model into a compact expression and set the baseline expression.

[0177] Step 3: After setting the baseline expression, approximate the feasible region of each flexibility resource within the polyhedral framework by shifting and scaling the baseline expression. To ensure that the inner approximation feasible region approximates the original feasible region of the flexibility resource as closely as possible, the objective function should be set to maximize the scaling factor, adjusted using a linear programming problem. For example... Figure 6 The graphical diagram shown illustrates that the inner approximation aggregation method mainly includes three steps: baseline setting, inner approximation, and Minkowski aggregation, which are performed sequentially. Figure 6 The process is executed.

[0178] Step 4: Use hierarchical clustering to partition distributed resources based on comprehensive similarity and distance similarity alone into clusters.

[0179] The clustering method was validated using a numerical example: 25 distributed energy storage units with different characteristics were selected as samples, and clustering analysis was performed on them under the hierarchical clustering framework using the proposed comprehensive similarity index to evaluate the compactness and clustering effect among individuals within each distributed energy cluster.

[0180] The 25 distributed energy storage parameter settings are shown in Table 1, where TGD is a truncated Gaussian distribution and UD is a uniform distribution.

[0181] Table 1

[0182]

[0183] By comparing the clustering of distributed resources that consider both geometric-distance similarity and distance similarity alone, the advantages of the proposed method in terms of clustering accuracy and flexibility are verified.

[0184] Since the constructed comprehensive similarity index is extracted based on the original technical constraint information of each distributed resource, the distance between distributed resources can directly reflect the degree of similarity between their technically feasible domains when clustering is performed under this similarity metric. Figure 7 As shown, in the clustering results that take into account comprehensive similarity, the clustering distance between distributed resources is significantly reduced, indicating that the method can group distributed resources with more similar technically feasible domain characteristics into the same cluster, thereby achieving a more accurate aggregation of the flexibility characteristics of distributed energy.

[0185] Step 5: After completing the clustering, perform flexible aggregation modeling on the DERs within each cluster according to the flexible aggregation method.

[0186] The Monte Carlo method was used to compare the volume of the approximate high-dimensional convex polyhedron obtained by aggregation with that of the high-dimensional convex polyhedron formed by each distributed resource under its original technical constraints, so as to evaluate the effectiveness of the proposed method in maintaining aggregation accuracy and flexibility.

[0187] like Figure 8 As shown, with the increase in the number of distributed resources, the flexible aggregation effect based on the comprehensive similarity index consistently outperforms the aggregation effect considering only the distance similarity index. Specifically, under different numbers of distributed resources, the ratio of the volume of the aggregated approximate high-dimensional convex polyhedron to the volume of its corresponding original high-dimensional convex polyhedron remains relatively stable and does not decrease significantly with the expansion of the aggregation scale. This indicates that the comprehensive similarity method can effectively maintain the flexibility and feasible region accuracy of the aggregation model while ensuring computational efficiency, demonstrating robustness and effectiveness in distributed energy aggregation scenarios of different scales.

[0188] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0189] Based on the same inventive concept, this application also provides a distributed resource feasible region aggregation apparatus for implementing the distributed resource feasible region aggregation method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more distributed resource feasible region aggregation apparatus embodiments provided below can be found in the limitations of the distributed resource feasible region aggregation method described above, and will not be repeated here.

[0190] In one exemplary embodiment, such as Figure 9 As shown, a distributed resource feasible domain aggregation device 900 is provided, including: a transformation module 902, a calculation module 904, an adjustment module 906, a clustering module 908, and an aggregation module 910, wherein:

[0191] The transformation module 902 is used to obtain multiple distributed resource models and transform the feasible domains of the multiple distributed resource models into compact expressions.

[0192] The calculation module 904 is used to calculate the reference coefficients based on all the coefficient matrices in the compact expression; and to substitute the reference coefficients into the compact expression to obtain the reference expression.

[0193] The adjustment module 906 is used to adjust the baseline expression with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and to obtain the inner approximate feasible regions of multiple distributed resource models.

[0194] Clustering module 908 is used to calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, the original feasible domains are clustered to obtain multiple sets of original feasible domains.

[0195] The aggregation module 910 is used to obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; and to aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0196] In one embodiment, the conversion module is further configured to obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; and to construct a compact expression for the multiple distributed resource models based on the coefficient matrix and parameter vector.

[0197] In one embodiment, the adjustment module is further configured to construct an objective function with the goal of maximizing the scaling factor, and obtain the constraints of the objective function based on a compact expression; under the constraints, solve the objective function to obtain a scaling factor with the goal of approximating the original feasible region of multiple distributed resource models; and adjust the baseline expression using the scaling factor to obtain the approximate feasible region of multiple distributed resource models.

[0198] In one embodiment, the clustering module is further configured to: calculate the directional similarity between feasible regions of multiple distributed resource models based on the coefficient matrix of the compact expression; calculate the distance similarity between feasible regions of multiple distributed resource models based on the parameter vector of the compact expression; construct a similarity matrix based on the directional similarity and the distance similarity; and use the similarity matrix as the similarity between feasible regions.

[0199] In one embodiment, the clustering module is further configured to cluster feasible domains based on similarity using a preset method and calculate preset coefficients; the preset coefficients are used to evaluate the clustering results; based on the preset coefficients, the optimal number of clusters is determined; and based on the optimal number of clusters and the clustering results, multiple sets of original feasible domains are obtained.

[0200] In one embodiment, the aggregation module is further configured to aggregate compact expressions in multiple sets of internal approximate feasible domains using a preset summation method to obtain an aggregated model of multiple distributed resource models.

[0201] Each module in the aforementioned distributed resource feasible domain aggregation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0202] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores distributed resource model data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a distributed resource feasible region aggregation method.

[0203] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0204] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0205] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0206] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0207] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0208] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0209] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0210] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on multiple distributed resource models, obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; and construct a compact expression for the multiple distributed resource models based on the coefficient matrix and parameter vector.

[0211] In one embodiment, when the processor executes the computer program, it further performs the following steps: constructing an objective function with the goal of maximizing the scaling factor, and obtaining the constraints of the objective function based on a compact expression; solving the objective function under the constraints to obtain a scaling factor with the goal of approximating the original feasible region of multiple distributed resource models; and adjusting the baseline expression using the scaling factor to obtain the approximate feasible region of multiple distributed resource models.

[0212] In one embodiment, when the processor executes the computer program, it further performs the following steps: calculating the directional similarity between feasible regions of multiple distributed resource models based on the coefficient matrix of the compact expression; calculating the distance similarity between feasible regions of multiple distributed resource models based on the parameter vector of the compact expression; constructing a similarity matrix based on the directional similarity and the distance similarity; and using the similarity matrix as the similarity between feasible regions.

[0213] In one embodiment, when the processor executes the computer program, it further performs the following steps: clustering feasible regions based on similarity using a preset method and calculating preset coefficients; using the preset coefficients to evaluate the clustering results; determining the optimal number of clusters based on the preset coefficients; and obtaining multiple sets of original feasible regions based on the optimal number of clusters and the clustering results.

[0214] In one embodiment, when the processor executes the computer program, it further performs the following steps: aggregating the compact expressions in multiple sets of inner approximate feasible domains using a preset summation method to obtain an aggregated model of multiple distributed resource models.

[0215] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0216] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0217] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0218] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0219] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0220] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0221] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on multiple distributed resource models, obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; and construct a compact expression for the multiple distributed resource models based on the coefficient matrix and parameter vector.

[0222] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing an objective function with the goal of maximizing the scaling factor, and obtaining the constraints of the objective function based on a compact expression; solving the objective function under the constraints to obtain a scaling factor with the goal of approximating the original feasible region of multiple distributed resource models; and adjusting the baseline expression using the scaling factor to obtain the approximate feasible region of multiple distributed resource models.

[0223] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the directional similarity between feasible regions of multiple distributed resource models based on the coefficient matrix of the compact expression; calculating the distance similarity between feasible regions of multiple distributed resource models based on the parameter vector of the compact expression; constructing a similarity matrix based on the directional similarity and the distance similarity; and using the similarity matrix as the similarity between feasible regions.

[0224] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: clustering feasible regions based on similarity using a preset method and calculating preset coefficients; using the preset coefficients to evaluate the clustering results; determining the optimal number of clusters based on the preset coefficients; and obtaining multiple sets of original feasible regions based on the optimal number of clusters and the clustering results.

[0225] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: aggregating the compact expressions in multiple sets of inner approximate feasible domains using a preset summation method to obtain an aggregated model of multiple distributed resource models.

[0226] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0227] Obtain multiple distributed resource models; transform the feasible domains of multiple distributed resource models into compact expressions;

[0228] The baseline coefficients are calculated from all the coefficient matrices in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression.

[0229] The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, and the inner approximate feasible regions of multiple distributed resource models are obtained.

[0230] Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains;

[0231] Obtain multiple inner approximate feasible domain sets corresponding to multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

[0232] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on multiple distributed resource models, obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; and construct a compact expression for the multiple distributed resource models based on the coefficient matrix and parameter vector.

[0233] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing an objective function with the goal of maximizing the scaling factor, and obtaining the constraints of the objective function based on a compact expression; solving the objective function under the constraints to obtain a scaling factor with the goal of approximating the original feasible region of multiple distributed resource models; and adjusting the baseline expression using the scaling factor to obtain the approximate feasible region of multiple distributed resource models.

[0234] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the directional similarity between feasible regions of multiple distributed resource models based on the coefficient matrix of the compact expression; calculating the distance similarity between feasible regions of multiple distributed resource models based on the parameter vector of the compact expression; constructing a similarity matrix based on the directional similarity and the distance similarity; and using the similarity matrix as the similarity between feasible regions.

[0235] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: clustering feasible regions based on similarity using a preset method and calculating preset coefficients; using the preset coefficients to evaluate the clustering results; determining the optimal number of clusters based on the preset coefficients; and obtaining multiple sets of original feasible regions based on the optimal number of clusters and the clustering results.

[0236] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: aggregating the compact expressions in multiple sets of inner approximate feasible domains using a preset summation method to obtain an aggregated model of multiple distributed resource models.

[0237] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0238] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0239] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0240] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for flexible aggregation of distributed resource models, characterized in that, The method includes: Obtain multiple distributed resource models; transform the feasible domain of the multiple distributed resource models into a compact expression; The baseline coefficients are calculated based on the coefficient matrix in the compact expression; the baseline coefficients are then substituted into the compact expression to obtain the baseline expression. The baseline expression is adjusted with the goal of constructing an inner approximation of the original feasible region of the distributed resource model, thereby obtaining the inner approximate feasible region of the multiple distributed resource models; Calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, cluster the original feasible domains to obtain multiple sets of original feasible domains; Obtain multiple inner approximate feasible domain sets corresponding to the multiple feasible domain sets; aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

2. The method according to claim 1, characterized in that, The step of transforming the feasible domains of the multiple distributed original resource models into compact expressions includes: Based on the multiple distributed resource models, obtain the coefficient matrix and parameter vector corresponding to the multiple distributed resource models; Based on the coefficient matrix and parameter vector, a compact expression for the multiple distributed resource models is constructed.

3. The method according to claim 1, characterized in that, The step of adjusting the baseline expression with the goal of constructing an inner approximation of the original feasible region of the distributed resource model to obtain the inner approximate feasible region of the multiple distributed resource models includes: Construct an objective function with the goal of maximizing the scaling factor, and obtain the constraints of the objective function based on the compact expression; Under the constraints of the above conditions, the objective function is solved to obtain a scaling factor that approximates the original feasible region of the multiple distributed resource models. The scaling factor is used to adjust the baseline expression to obtain the inner approximate feasible region of the multiple distributed resource models.

4. The method according to claim 1, characterized in that, The calculation of the similarity between the original feasible domains of multiple distributed resource models includes: Based on the coefficient matrix of the compact expression, calculate the directional similarity between the feasible regions of the multiple distributed resource models; Based on the parameter vector of the compact expression, calculate the distance similarity between the feasible regions of the multiple distributed resource models; Based on the directional similarity and the distance similarity, a similarity matrix is ​​constructed; the similarity matrix is ​​used as the similarity between the original feasible regions.

5. The method according to claim 1, characterized in that, Based on the similarity, the original feasible domains are clustered to obtain multiple sets of original feasible domains, including: Based on the similarity, the feasible region is clustered using a preset method, and preset coefficients are calculated; the preset coefficients are used to evaluate the clustering results. Based on the preset coefficients, determine the optimal number of clusters; Based on the optimal number of clusters and the clustering results, multiple original feasible domain sets are obtained.

6. The method according to claim 1, characterized in that, The aggregation of the multiple sets of approximate feasible internal domains to obtain an aggregated model of multiple distributed resource models includes: The compact expressions in the multiple sets of inner approximate feasible regions are aggregated using a preset summation method to obtain an aggregated model of multiple distributed resource models.

7. A distributed resource model flexibility aggregation device, characterized in that, The device includes: The transformation module is used to obtain multiple distributed resource models and transform the feasible domains of the multiple distributed resource models into compact expressions. The calculation module is used to calculate the reference coefficients based on the coefficient matrix in the compact expression; and to substitute the reference coefficients into the compact expression to obtain the reference expression. The adjustment module is used to adjust the baseline expression with the goal of approximating the inner feasible region of the original feasible region of the distributed resource model, and to obtain the inner approximate feasible region of the multiple distributed resource models. A clustering module is used to calculate the similarity between the original feasible domains of multiple distributed resource models; based on the similarity, the original feasible domains are clustered to obtain multiple sets of original feasible domains; The aggregation module is used to obtain multiple inner approximate feasible domain sets corresponding to the multiple feasible domain sets; and to aggregate the multiple inner approximate feasible domain sets to obtain an aggregated model of multiple distributed resource models.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images