A distributed energy storage aggregation method and system based on improved minkowski sum

Abstract

This invention discloses a distributed energy storage aggregation method and system based on an improved Minkowski sum, relating to the field of power system technology. The method includes: constructing individual unit operation models; characterizing the power regulation feasible region of each energy storage unit in a Polytope half-plane form based on its operation model; clustering the distributed energy storage cluster to be aggregated based on the model parameters of each individual unit in the cluster, determining the clustering results; and aggregating the power regulation feasible regions of all individual energy storage units in each category of distributed energy storage using an improved internal approximation Minkowski sum, determining the aggregation feasible region of each clustering result of the energy storage cluster to be aggregated. This invention improves the aggregation efficiency of a large number of distributed flexible resources, reduces the flexibility loss during aggregation, enhances the regulation capability of the power system, and provides positive support for the sustainable development and economic operation of the power system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a distributed energy storage aggregation method and system based on an improved Minkowski sum. Background Technology

[0002] With the continuous advancement of the electricity substitution strategy, electricity load is increasing year by year, posing unprecedented challenges to the safe and economical operation of the power system. Faced with increasingly severe energy demands, effectively tapping into and mobilizing distributed energy storage resources on the user side to participate in peak shaving is particularly important. This can not only reduce reliance on traditional power investment and decrease the peak-valley difference in the power system, but also significantly improve the operational safety and economy of the power system. Although the adjustable power of individual energy storage devices is usually small, making it difficult to play a significant role in large-scale peak shaving, distributed energy storage groups exhibit unique advantages: large numbers, flexible dispatching methods, and enormous potential. Therefore, how to effectively aggregate the adjustable capabilities of distributed energy storage resources and achieve efficient dispatching and management has become a pressing technical challenge for the power system. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is: how to solve the problems of difficult coordination and scheduling among multiple energy storage units, inaccurate power regulation, and mutual interference between clusters in existing distributed energy storage systems.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a distributed energy storage aggregation method based on an improved Minkowski sum, comprising the following steps,

[0006] Collect parameter data based on user-side distributed energy storage;

[0007] A single-unit operation model is constructed based on the operating principle of distributed energy storage;

[0008] Based on the operating model of each energy storage unit, the feasible domain of power regulation is characterized in the form of a half-plane of Polytopes.

[0009] Based on the model parameters of each distributed energy storage unit in the distributed energy storage cluster to be aggregated, the distributed energy storage cluster to be aggregated is clustered to determine the clustering result of the distributed energy storage cluster to be aggregated.

[0010] Based on the clustering results, the improved internal approximation Minkowski method and the power regulation feasible region of all individual energy storage units in each type of distributed energy storage are aggregated to determine the aggregation feasible region of each clustering result of the energy storage cluster to be aggregated.

[0011] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, the parameter data includes the capacity, rated power, charge / discharge efficiency, and initial SOC of each distributed energy storage unit.

[0012] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, the single-unit operation model is expressed as:

[0013]

[0014] in, Let n be the energy storage level of energy storage device n at time t; and These are charging efficiency and discharging efficiency, respectively. and Let be the charging and discharging power of energy storage device n at time t; and These are the minimum and maximum charging power values ​​of energy storage device n, respectively. and These are the minimum and maximum discharge power of energy storage device n, respectively; and Let B be the minimum and maximum capacity of energy storage device n, respectively; ESSc,t and B ESSdis,t It is a charge / discharge state variable, which is a 0-1 variable.

[0015] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, wherein: the power regulation feasible region is characterized in the form of a Polytope half-plane by representing the power feasible region of the energy storage device n in the form of a Polytope half-plane, and the expression is:

[0016]

[0017] in, and These represent the power feasible region and the power matrix within the scheduling period of energy storage device n, respectively. and These are the inequality coefficient matrix and column vector, respectively, characterizing the feasible region of the energy storage device n; Indicates power constraint. Represents the energy constraint; E is the identity matrix. It is determined by the charging and discharging efficiency.

[0018] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, the step of clustering the distributed energy storage cluster to be aggregated according to the model parameters of each distributed energy storage unit in the cluster to be aggregated, and determining the clustering result of the distributed energy storage cluster to be aggregated, includes constructing a data sample set according to the model parameters of each distributed energy storage unit in the cluster to be aggregated, and normalizing the initial data; setting the neighborhood radius r and the minimum density M. min Randomly select a point O that has not been clustered. 1 according to Calculation and O 1 The number M of points whose Euclidean distance is less than r l , with O 1 They are grouped into one category;

[0019] Among them, D E (x1, x2) is the Euclidean distance between vectors x1 and x2; 1,i and x 2,i I is the i-th dimension of vectors x1 and x2; I is the total dimension.

[0020] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, the determination of the clustering results of the distributed energy storage cluster to be aggregated further includes,

[0021] When M l ≥M min When traversing other points in the current class, calculate the number of unclustered points in the neighborhood r of each point, and include the unclustered points in the current class. If the number of unclustered points in the neighborhood of all points in the current class is less than M, then... min If no clustering points are found, the system will re-evaluate whether there are still clustering points and continue to calculate and classify. Otherwise, it will re-traverse the other points in the current class and recalculate the number of unclustered points in the neighborhood r of each point.

[0022] When M l <M min If the condition is met, determine whether there are still cluster points and continue calculating the number M. l And classify them.

[0023] As a preferred embodiment of the distributed energy storage aggregation method based on the improved Minkowski sum described in this invention, the improved inner approximation Minkowski sum is used to aggregate the power adjustment feasible domains of all individual energy storage units in each type of distributed energy storage, and the aggregation feasible domain of each clustering result of the energy storage cluster to be aggregated includes,

[0024] Based on the energy storage operation model, the original feasible region of a single energy storage unit is obtained. Parameters are selected and clustered using DBSCAN to form multiple energy storage clusters;

[0025] Calculate the initial baseline for each cluster. and Solving the optimization problem yields the approximate feasible region for each individual energy storage unit within the cluster. The expression is:

[0026]

[0027] Obtain the aggregate feasible domain of the current cluster Based on the calculated and recorded accuracy metrics for each cluster, the expression is:

[0028]

[0029] Change the A0 of each cluster, perform the inner approximation again and record the accuracy index. After traversing the A0 of each cluster, form the final aggregated feasible region of each cluster with the highest accuracy.

[0030] Another objective of this invention is to provide a distributed energy storage aggregation system based on an improved Minkowski sum. This system can collect the operating parameters of individual energy storage units, construct a polyhedral power regulation model, classify different individual energy storage devices based on a clustering algorithm, and use an improved inner approximation Minkowski sum algorithm to achieve optimized aggregation of the power feasible region. This effectively solves the problems of scheduling incoordination, insufficient power regulation accuracy, poor system scalability and robustness caused by large differences in energy storage device parameters in the prior art.

[0031] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a distributed energy storage aggregation system based on improved Minkowski sums, comprising: a data collection and preprocessing module, a single-unit operation model construction module, a cluster analysis module, and a Minkowski sum aggregation and optimization module;

[0032] The data collection and preprocessing module collects parameter data from distributed energy storage devices on the user side and cleans and standardizes the collected data.

[0033] The single-unit operation model construction module establishes a mathematical model for each energy storage unit based on the operating principle of distributed energy storage.

[0034] The clustering analysis module automatically clusters energy storage devices based on the model parameters and operating status of each energy storage unit. It uses the DBSCAN clustering algorithm to group energy storage units with similar characteristics into the same cluster. The neighborhood radius and minimum density of the clustering algorithm are set to complete the adaptive classification.

[0035] The Minkowski sum aggregation and optimization module uses an improved inner approximation Minkowski sum algorithm to aggregate the feasible regions of individual power adjustment within each cluster, forming an aggregated feasible region of the cluster. It calculates the accuracy index multiple times and performs inner approximation optimization, ultimately achieving the optimal level of accuracy for the aggregated feasible region.

[0036] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a distributed energy storage aggregation method based on an improved Minkowski sum as described above.

[0037] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a distributed energy storage aggregation method based on an improved Minkowski sum as described above.

[0038] The beneficial effects of this invention are as follows: Addressing the unprecedented challenges posed by the high proportion of renewable energy grid connection and the annual increase in electricity load to the safe and economical operation of the power system, this invention proposes a distributed energy storage cluster aggregation method based on an improved internal approximation Minkowski sum. Compared with existing technologies, this invention improves the aggregation efficiency of a large number of distributed flexible resources, reduces the flexibility loss during the aggregation process, enhances the regulation capability of the power system, and provides positive support for the sustainable development and economical operation of the power system. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:

[0040] Figure 1 The overall flowchart of a distributed energy storage aggregation method based on improved Minkowski sums is provided for the first embodiment of the present invention;

[0041] Figure 2 A schematic diagram of distributed energy storage clustering results in a distributed energy storage aggregation method based on improved Minkowski sums provided in the second embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the power and capacity aggregation results of a distributed energy storage cluster 1 based on an improved Minkowski distribution-based distributed energy storage aggregation method, provided for the second embodiment of the present invention.

[0043] Figure 4This is a schematic diagram of the power and capacity aggregation results of a distributed energy storage cluster 2 based on an improved Minkowski distribution-based distributed energy storage aggregation method, provided as a second embodiment of the present invention. Detailed Implementation

[0044] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0045] Example 1, referring to Figure 1 As one embodiment of the present invention, a distributed energy storage aggregation method based on an improved Minkowski sum is provided, comprising:

[0046] Step 1: Based on the operating principles of distributed energy storage, construct its single-unit operation model as follows:

[0047]

[0048]

[0049] In the formula, Let n be the energy storage level of energy storage device n at time t; and These are charging efficiency and discharging efficiency, respectively. and Let be the charging and discharging power of energy storage device n at time t; and These are the minimum and maximum charging power values ​​of energy storage device n, respectively. and These are the minimum and maximum discharge power of energy storage device n, respectively; and Let B be the minimum and maximum capacity of energy storage device n, respectively; ESSc,t and B ESSdis,t It is a charge / discharge state variable, which is a 0-1 variable.

[0050] Step two, the feasible power regulation region of the operating model of each energy storage cell, represented by a Polytope half-plane, is as follows:

[0051]

[0052] In the formula, and These represent the power feasible region and the power matrix within the scheduling period of energy storage device n, respectively. and These are the inequality coefficient matrix and column vector, respectively, characterizing the feasible region of the energy storage device n; Indicates power constraint. Represents the energy constraint; E is the identity matrix. Determined by charge and discharge efficiency; the specific meaning is as follows:

[0053]

[0054]

[0055] Step 3: Based on the model parameters of each distributed energy storage unit in the distributed energy storage cluster to be aggregated, cluster the distributed energy storage cluster to be aggregated, and determine the clustering results of the distributed energy storage cluster to be aggregated, including:

[0056] Step 1: Based on the model parameters of each individual distributed energy storage unit in the distributed energy storage cluster to be aggregated, construct a data sample set and normalize the initial data;

[0057] Step 2: Set the neighborhood radius r and the minimum density M min ;

[0058] Step 3: Randomly select a point O that has not been clustered. 1 according to Calculation and O 1 The number M of points whose Euclidean distance is less than r l and connect these points with O 1 They are clustered into one category, of which: D E (x1, x2) is the Euclidean distance between vectors x1 and x2; 1,i and x 2,i I is the i-th dimension of vectors x1 and x2; I is the total dimension.

[0059] Step 4: If M l ≥M min Proceed to step 5; otherwise proceed to step 7.

[0060] Step 5: Traverse the other points in the current class, calculate the number of unclustered points in the neighborhood r of each point, and include these unclustered points in the current class;

[0061] Step 6: If the number of unclustered points in the neighborhood of all points in the current class is less than M min Proceed to step 7; otherwise return to step 5.

[0062] Step 7: If there are still unclustered points, return to step 3; otherwise, clustering is complete.

[0063] Step four involves aggregating the feasible power regulation domains of all individual energy storage units in each type of distributed energy storage using an improved internal approximation Minkowski method, including:

[0064] An inner approximation is performed for the power regulation feasible region of all individual energy storage units in each type of distributed energy storage cluster:

[0065]

[0066] In the formula, For an individual n, σ is an approximate constraint space. n Let n be the scaling factor for individual n; Let n be the translation factor for individual n. Its specific value can be obtained by solving the following LP problem:

[0067]

[0068] In the formula, α n =1 / σ n ; N represents the number of distributed energy storage devices in this type of cluster.

[0069] For each type of distributed energy storage cluster, the approximate power adjustment feasible region of all individual energy storage units is calculated using the Minkowski sum to determine the aggregation feasible region of each clustering result of the energy storage cluster to be aggregated:

[0070]

[0071] In the formula, Ω agg This represents the feasible region for power aggregation in distributed energy storage clusters.

[0072] To reduce the flexibility lost during the internal approximation process, the following accuracy index is defined, and the approximation degree is improved by changing the baseline A0 based on this index:

[0073]

[0074] In the formula, N f The number of unit normal vectors; Δ C,l and Δ A,l , respectively, are the widths of the original feasible region and the approximate feasible region along the direction of the l-th unit normal vector.

[0075] Accuracy index calculation:

[0076] Let Δ O The maximum distance between two tangent planes along each unit normal vector direction of the original feasible region can be solved using optimization methods. The original feasible region O is represented by a half-space polyhedron as follows:

[0077] O={x∈R N :Ax≤b} (16)

[0078] For a certain unit normal vector f l =[a1,a2,…,a N The corresponding N-dimensional hyperplane is:

[0079] a1x1 + a2x2 + ... + a N a N +b l =0 (17)

[0080] In the formula, x1, x2, ..., x N Position in each dimension; b l It is a constant.

[0081] Assumption:

[0082] X P =[x 1p ,x 2p ,…,x Np (18)

[0083] X Q =[x 1q ,x 2q ,…,x Nq (19)

[0084] In the formula, X P and X Q Let these represent a point outside the hyperplane and a point inside the hyperplane, respectively.

[0085] The unit normal vector f l Δ on O,l The solution can then be obtained using the following optimization model:

[0086]

[0087] In summary, the following method is designed based on the improved inner approximation Minkowski method and feasible region aggregation:

[0088]

[0089] Thus, the distributed energy storage cluster aggregation model based on the improved inner approximation Minkowski sum has been established and can be applied to practical examples.

[0090] Therefore, based on the aggregated feasible domain of each clustering result of the distributed energy storage cluster to be aggregated, the adjustability of the distributed energy storage cluster to be aggregated can be evaluated. In addition, the aggregated feasible domain of the distributed energy storage cluster to be aggregated can be submitted to the power grid dispatch center for dispatching strategy.

[0091] Example 2, an embodiment of the present invention, provides a system based on an improved Minkowski sum-based distributed energy storage aggregation method, comprising: a data collection and preprocessing module, a single-unit operation model construction module, a cluster analysis module, and a Minkowski sum aggregation and optimization module;

[0092] The data collection and preprocessing module collects parameter data from distributed energy storage devices on the user side and cleans and standardizes the collected data.

[0093] The single-unit operation model building module establishes a mathematical model for each energy storage unit based on the operating principle of distributed energy storage;

[0094] The clustering analysis module automatically clusters energy storage devices based on the model parameters and operating status of each energy storage unit. Using the DBSCAN clustering algorithm, energy storage units with similar characteristics are grouped into the same cluster. The neighborhood radius and minimum density of the clustering algorithm are set to complete the adaptive classification.

[0095] The Minkowski sum aggregation and optimization module uses an improved inner approximation Minkowski sum algorithm to aggregate the feasible regions of individual power adjustment within each cluster, forming an aggregated feasible region of the cluster. It calculates the accuracy index multiple times and performs inner approximation optimization, ultimately achieving the optimal level of accuracy for the aggregated feasible region.

[0096] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0099] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0100] Example 3: In this example, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. This example compares the existing conventional methods with the method of this example.

[0101] This invention aggregates 50 distributed energy storage systems, and the parameter distribution range is shown in Table 1.

[0102] Table 1 Distribution of Distributed Energy Storage Parameters

[0103]

[0104] Table 2 shows the calculation results of the accuracy index before and after the improvement. The accuracy index of distributed energy storage cluster 1 before improvement is 85.41%, and the accuracy index after improvement is 90.57%, with an improvement of 5.16%. The accuracy index of distributed energy storage cluster 2 before improvement is 95.80%, and the accuracy index after improvement is 98.25%, with an improvement of 2.45%. It can be seen that the method proposed in this invention can effectively reduce the flexibility lost in the approximation process and improve the approximation degree.

[0105] Table 2 Calculation Results of Accuracy Index

[0106]

[0107] Figure 2 For the distributed energy storage clustering results, clustering parameter 1 is selected as the energy / power ratio, which reflects the time required for the energy storage to be fully charged from the rated power. Clustering parameter 2 is selected as the charge and discharge efficiency, which determines the shape of the feasible domain A matrix. The number of clusters 1 is 43 and the number of clusters 2 is 7.

[0108] Figure 3 The power and capacity aggregation results for distributed energy storage cluster 1 are shown below. According to the aggregation results, the upper limit of charging power for cluster 1 is 6323.84kW, the lower limit of discharging power is -6315.55kW, the upper limit of capacity is 15471.47kWh, and the lower limit of capacity is -3726.65kWh. It is evident that distributed energy storage, after aggregation, possesses strong charging, discharging, and energy storage capabilities, enabling it to absorb a large amount of energy during charging for release during peak grid demand.

[0109] Figure 4 The power and capacity aggregation results for distributed energy storage cluster 2 are as follows: According to the aggregation results, the upper limit of charging power of cluster 2 is 197.92kW, the lower limit of discharging power is -197.72kW, the upper limit of capacity is 1110.39kWh, and the lower limit of capacity is -274.42kWh.

[0110] It should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed energy storage aggregation method based on improved Minkowski sums, characterized in that, include: Collect parameter data based on user-side distributed energy storage; A single-unit operation model is constructed based on the operating principle of distributed energy storage; Based on the operating model of each energy storage unit, the feasible domain of power regulation is characterized in the form of a half-plane of Polytopes. Based on the model parameters of each distributed energy storage unit in the distributed energy storage cluster to be aggregated, the distributed energy storage cluster to be aggregated is clustered to determine the clustering result of the distributed energy storage cluster to be aggregated. Based on the clustering results, the improved inner approximation Minkowski and the power adjustment feasible region of all individual energy storage units in each type of distributed energy storage are aggregated to determine the aggregation feasible region of each clustering result of the energy storage cluster to be aggregated. The improved internal approximation Minkowski method and the aggregation of the power adjustment feasible domains of all individual energy storage units in each type of distributed energy storage determine the aggregation feasible domain of each clustering result of the energy storage cluster to be aggregated, including: Based on the energy storage operation model, the original feasible region of a single energy storage unit is obtained. Parameters are selected and clustered using DBSCAN to form multiple energy storage clusters; Calculate the initial baseline for each cluster. and Solving the optimization problem yields the approximate feasible region for each individual energy storage unit within the cluster. The expression is: Obtain the aggregate feasible domain of the current cluster Based on the calculated and recorded accuracy metrics for each cluster, the expression is: Change the A0 of each cluster, perform the inner approximation again and record the accuracy index. After traversing the A0 of each cluster, form the final aggregated feasible region of each cluster with the highest accuracy.

2. The distributed energy storage aggregation method based on the improved Minkowski sum as described in claim 1, characterized in that: The parameter data includes the capacity, rated power, charge / discharge efficiency, and initial SOC of each distributed energy storage system.

3. The distributed energy storage aggregation method based on the improved Minkowski sum as described in claim 2, characterized in that: The single-unit operation model is expressed as follows: in, Let n be the energy storage level of energy storage device n at time t; and These are charging efficiency and discharging efficiency, respectively. and Let be the charging and discharging power of energy storage device n at time t; and These are the minimum and maximum charging power values ​​of energy storage device n, respectively. and These are the minimum and maximum discharge power of energy storage device n, respectively; and Let B be the minimum and maximum capacity of energy storage device n, respectively; ESSc,t and B ESSdis,t , is a 0-1 variable.

4. The distributed energy storage aggregation method based on the improved Minkowski sum as described in claim 3, characterized in that: The description of the power regulation feasible region in Polytopes half-plane form represents the power feasible region of energy storage device n using Polytopes half-plane, expressed as: in, and These represent the power feasible region and the power matrix within the scheduling period of energy storage device n, respectively. and These are the inequality coefficient matrix and column vector, respectively, characterizing the feasible region of the energy storage device n; Indicates power constraint. Represents the energy constraint; E is the identity matrix. It is determined by the charging and discharging efficiency.

5. The distributed energy storage aggregation method based on the improved Minkowski sum as described in claim 4, characterized in that: The step of clustering the distributed energy storage cluster to be aggregated according to the model parameters of each distributed energy storage unit in the distributed energy storage cluster to be aggregated, and determining the clustering result of the distributed energy storage cluster to be aggregated, includes constructing a data sample set according to the model parameters of each distributed energy storage unit in the distributed energy storage cluster to be aggregated, and normalizing the initial data. Define the neighborhood radius r and the minimum density M. min Randomly select a point O that has not been clustered. 1 according to Calculation and O 1 The number M of points whose Euclidean distance is less than r l , with O 1 They are grouped into one category; Among them, D E (x1, x2) is the Euclidean distance between vectors x1 and x2; 1,i and x 2,i I is the i-th dimension of vectors x1 and x2; I is the total dimension.

6. The distributed energy storage aggregation method based on the improved Minkowski sum as described in claim 5, characterized in that: The clustering results for determining the distributed energy storage clusters to be aggregated also include, When M l ≥M min When traversing other points in the current class, calculate the number of unclustered points in the neighborhood r of each point, and include the unclustered points in the current class. If the number of unclustered points in the neighborhood of all points in the current class is less than M, then... min If no clustering points are found, the system will re-evaluate whether there are still clustering points and continue to calculate and classify. Otherwise, it will re-traverse the other points in the current class and recalculate the number of unclustered points in the neighborhood r of each point. When M l <M min If the condition is met, determine whether there are still cluster points and continue calculating the number M. l And classify them.

7. A system employing a distributed energy storage aggregation method based on an improved Minkowski sum as described in any one of claims 1 to 6, characterized in that: It includes modules for data collection and preprocessing, individual operational model construction, cluster analysis, Minkowski analysis, aggregation, and optimization. The data collection and preprocessing module collects parameter data from distributed energy storage devices on the user side and cleans and standardizes the collected data. The single-unit operation model construction module establishes a mathematical model for each energy storage unit based on the operating principle of distributed energy storage. The clustering analysis module automatically clusters energy storage devices based on the model parameters and operating status of each energy storage unit. It uses the DBSCAN clustering algorithm to group energy storage units with similar characteristics into the same cluster. The neighborhood radius and minimum density of the clustering algorithm are set to complete the adaptive classification. The Minkowski sum aggregation and optimization module uses an improved inner approximation Minkowski sum algorithm to aggregate the feasible regions of individual power adjustment within each cluster, forming an aggregated feasible region of the cluster. It calculates the accuracy index multiple times and performs inner approximation optimization, ultimately achieving the optimal level of accuracy for the aggregated feasible region.

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 any one of claims 1 to 6 of the distributed energy storage aggregation method based on the improved Minkowski method.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed energy storage aggregation method based on the improved Minkowski sum as described in any one of claims 1 to 6.

Images