Distributed energy storage iteration clustering rapid regulation and control method and system

Abstract

The invention relates to a distributed energy storage iterative clustering rapid regulation and control method, which comprises the following steps: firstly, obtaining the regulation precision and regulation rate of each distributed energy storage, then selecting part of the distributed energy storage for clustering, and obtaining a plurality of clustered distributed energy storage clusters; a clustering objective function of clustering is set as the weighted sum of the adjustment precision and the adjustment rate; and then a distributed energy storage cluster is used for regulation and control. According to the method and system provided by the invention, the computing resource overhead of clustering and regulation and control instruction decomposition is reduced, and the adverse effects caused by communication delay and individual performance difference are effectively overcome, so that the whole distributed energy storage polymer can respond to the power grid dispatching instruction more quickly and more accurately; and application scenes such as frequency modulation with relatively high requirements on rapidity and accuracy can be met.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage scheduling, and specifically relates to a distributed energy storage iterative clustering rapid control method and system. Background Art

[0002] In recent years, distributed energy storage resources, represented by household energy storage and 5G base station energy storage, have experienced explosive growth. These large and geographically dispersed energy storage units have formed a new type of distributed energy storage resource pool with huge potential on the load side. At the same time, the power system is also facing increasingly severe challenges, including the volatility and intermittency problems caused by the grid connection of new energy (such as wind power and photovoltaics), the increase in the peak-to-valley difference of the power grid, and the increasing demand for auxiliary services such as frequency regulation.

[0003] Effectively clustering and collaboratively controlling massive distributed energy storage resources so that they can participate in grid interaction can not only meet the energy needs of base stations and users themselves (such as utilizing the difference in peak-valley electricity prices for economical charging and discharging, and serving as a backup power source), but can also provide the grid with key services such as peak shaving, frequency regulation, and voltage support, which is of great significance for improving the flexibility, safety, and economy of the grid.

[0004] To this end, the industry has proposed some technical solutions for distributed energy storage clustering and regulation. For example, business models such as resource clusterers and virtual power plants (VPPs) have gradually emerged, aiming to integrate scattered resources to participate in the market or accept grid dispatch.

[0005] However, when the above-mentioned existing technologies are applied to actual scenarios with large-scale and high dynamic demands, especially when distributed energy storage is required to provide fast-response frequency regulation and other services, their inherent defects gradually become apparent. For example, there are problems such as slow response speed and poor consistency, difficulty in ensuring regulation accuracy, complex scheduling strategies and heavy computational burden.

[0006] Therefore, the existing distributed energy storage clustering control technology has obvious deficiencies in response speed, regulation accuracy, control complexity and potential mining, and it is difficult to adapt to the trend of high proportion of new energy access and growing demand for grid interaction in the future. Therefore, it is urgent to study a distributed energy storage iterative clustering fast control method that can comprehensively consider the dynamic regulation performance of distributed energy storage, effectively deal with communication delays and individual differences, and reduce control complexity, so as to achieve fast, accurate and efficient coordinated control of massive distributed energy storage resources. Summary of the invention

[0007] One of the purposes of the present invention is to solve at least one or more of the above-mentioned problems existing in the prior art. In other words, one of the purposes of the present invention is to provide a distributed energy storage iterative clustering rapid control method and system that meets one or more of the above-mentioned requirements.

[0008] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distributed energy storage iterative clustering rapid control method, comprising: S1. Obtaining the regulation accuracy and regulation rate of each distributed energy storage; S2, selecting a part from the distributed energy storage for clustering, and obtaining a number of distributed energy storage clusters after clustering, wherein the clustering objective function of the clustering is the weighted sum of the regulation accuracy and the regulation rate; S3. Use distributed energy storage clusters for regulation.

[0009] As a preferred embodiment, the method further comprises the steps of: S4. During the control process, the total clustering error of all distributed energy storage is monitored. If the change rate of the total clustering error exceeds a preset value, return to step S2 for re-clustering.

[0010] As a preferred implementation, step S2 includes: S21, selecting several distributed energy storages as initial cluster centers; S22, allocating the remaining distributed energy storage to the nearest initial cluster center to generate a number of energy storage clusters, where the distance is the difference of the clustering objective function; S23, calculating the sum of the distances from each distributed energy storage in each energy storage cluster to the other distributed energy storages, and selecting the distributed energy storage with the smallest sum of the distances as the cluster center of the energy storage cluster as a single sample; S24, repeat steps S21-S23 for several samplings, and select the comprehensive distance of all energy storage clusters and the smallest primary solution as the result of this clustering iteration, to obtain several clustered distributed energy storage clusters.

[0011] As a preferred implementation, step S3 includes: S31. In response to the total control instruction, calculate the control instruction of each distributed energy storage cluster; S32. Regulate each distributed energy storage according to the control instructions.

[0012] As a further preferred implementation, step S31 includes: S311. Calculate the constraints of the distributed energy storage cluster; S312, in response to the general regulation instruction, calculating the regulation margin allocated to each distributed energy storage cluster according to the regulation performance of each distributed energy storage cluster and the total regulation performance of all distributed energy storage clusters; S313 regulates each distributed energy storage according to the regulation margin allocated to each distributed energy storage cluster.

[0013] As a further preferred implementation, when regulating each distributed energy storage in the distributed energy storage cluster according to the regulation margin allocated to each distributed energy storage cluster, an equal margin regulation method is used.

[0014] As a further preferred implementation, the equal margin control method is specifically as follows: Obtaining a positive regulation margin or a negative regulation margin of each distributed energy storage; The power of each distributed energy storage is calculated according to the proportion of the positive regulation margin or the negative regulation margin in the regulation margin of the distributed energy storage cluster.

[0015] As a preferred implementation, the regulation accuracy of each distributed energy storage is obtained by dividing the target power instruction sent to each distributed energy storage by the actual stable output active power of the distributed energy storage.

[0016] As a preferred implementation, the regulation rate of each distributed energy storage is calculated according to the power change rate of each distributed energy storage from the initial moment to the regulation stable moment.

[0017] On the other hand, the present invention provides a distributed energy storage iterative clustering rapid control system, which uses any of the distributed energy storage iterative clustering rapid control methods described above to control distributed energy storage.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The distributed energy storage iterative clustering rapid control method of the present invention adopts a performance index that comprehensively considers the control speed and control accuracy as the clustering basis, which reduces the computational burden of the clustering algorithm, and divides the energy storage units with similar actual dynamic response characteristics into the same cluster, thereby improving the clustering speed and realizing accurate clustering of the actual control performance. On this basis, the dispatching instructions are allocated according to the actual performance margin of each cluster, so that clusters with superior performance can take on more regulation tasks, while clusters with poor performance avoid overload. This clustering and differentiated instruction allocation based on actual dynamic performance reduces the resource overhead when instructions are decomposed, and effectively overcomes the adverse effects of communication delays and individual performance differences, so that the distributed energy storage aggregate as a whole can respond to power grid dispatching instructions faster and more accurately, meeting application scenarios such as frequency modulation that require high speed and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the clustering iteration process of the distributed energy storage iterative clustering rapid control method according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the three-layer structure of the distributed energy storage iterative clustering rapid control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0021] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted or combined. In addition, the features described in some examples may be combined in other examples.

[0022] An embodiment of the present invention provides a distributed energy storage iterative clustering rapid control method, comprising the following steps: S1. Obtain the regulation accuracy and regulation rate of each distributed energy storage.

[0023] By comparing the target power command sent to each distributed energy storage with the active power actually output by the distributed energy storage, the regulation accuracy of the i-th distributed energy storage can be obtained. Calculated by the following formula:

[0024] in is the target power instruction sent to the i-th distributed energy storage in a regulation process, is the active power actually stably output by the i-th distributed energy storage after receiving the target power command.

[0025] The power change rate of each distributed energy storage from the initial moment to the moment of stable regulation is the regulation rate of the distributed energy storage. Therefore, for the i-th distributed energy storage, its regulation rate is Calculated by the following formula:

[0026] in, is the initial moment when the adjustment instruction is received, To adjust the stability moment, is the actual power at the initial moment, To adjust the active power at stable moments.

[0027] S2. Select a portion from the distributed energy storage for clustering to obtain a plurality of clustered distributed energy storage clusters.

[0028] The clustering objective function of the clustering operation in step S2 is the weighted sum of the adjustment accuracy and the adjustment rate. Specifically, the calculation formula of the clustering objective function is set to: ; The clustering objective function sets weights for adjustment accuracy and adjustment rate, including the accuracy coefficient K， Rate coefficient J ,and K ≤1 ，J ≤1.

[0029] The clustering objective function forms a combined performance index that can better meet the needs of the power grid and demonstrate comprehensive regulation performance. By clustering based on comprehensive regulation performance, it is ensured that the aggregated resource pool has better response speed and higher regulation accuracy as a whole.

[0030] In the subsequent clustering process, the clustering objective function can reflect the comprehensive regulation performance gap between distributed energy storages, so that distributed energy storages are more inclined to aggregate distributed energy storages with similar levels of quality into a cluster during the aggregation process, so that the regulation performance of each cluster is unified.

[0031] At the same time, the traditional clustering method for distributed energy storage generally considers optimization in terms of peak shaving and valley filling, electricity purchase costs, etc., focusing on the absolute optimum of the whole and the whole, which will lead to slow convergence of clustering calculations. The clustering objective function of step S2 focuses on the adjustment speed and adjustment accuracy, which not only has excellent adjustment performance, but also the clustering index calculation is simple and the iteration is fast. In this way, the control lag and error caused by the traditional method are avoided, so that the overall adjustment speed and accuracy are improved.

[0032] A preferred embodiment of the present invention provides a specific implementation of clustering in step S2, including the following steps: S21. Select several distributed energy storages as initial cluster centers and set a clustering objective function.

[0033] Step S21 first sets the cluster center for the subsequent clustering algorithm. Specifically, step S2 sets the set of m distributed energy storage as B i , randomly select a distributed energy storage cluster D of size d from these m distributed energy storages i , and then in the distributed energy storage cluster D i In the random selection of n distributed energy storage as the initial clustering centers, the set of initial clustering centers is set to Q i , select the actual distributed energy storage set Q i As the center point, it can resist the influence of outliers, and the cluster center has a clear physical meaning.

[0034] Specifically, .

[0035] Based on performance indicators and using actual data points as the initial center, the subsequent clustering results can more effectively divide distributed energy storage units with similar regulation performance into the same cluster.

[0036] S22, allocating the remaining distributed energy storage to the nearest initial cluster center to generate a number of energy storage clusters, where the distance is the difference of the clustering objective function; The purpose of this step is to perform a preliminary division of the distributed energy storage units based on the initial clustering centers selected in step S2 and the defined clustering objective function, and to generate energy storage clusters for each initial clustering center.

[0037] First, consider the distributed energy storage that needs to be allocated. In a preferred embodiment, in order to improve the efficiency of processing large-scale data, the allocated object is the sample cluster D randomly selected from all m distributed energy storages in step S21. i Of course, in other implementations, all m distributed energy storages may be directly allocated.

[0038] The following is D i Step S3 is described using the example.

[0039] For D i Each distributed energy storage , calculate each distributed energy storage to each initial cluster center distance, and then assign each distributed energy storage to the nearest initial cluster center , with each Multiple energy storage clusters are formed for the cluster center. The number of clusters is proportional to the initial cluster center. The number n is the same.

[0040] The distance here is the difference between the clustering objective function. The preferred calculation method given in this embodiment is the difference between the distributed energy storage and the initial cluster center. The absolute difference between the two clustering objective function values.

[0041] The above method forms n energy storage clusters , Represents each distributed energy storage To the initial cluster center distance.

[0042] S23, calculating the sum of distances from each distributed energy storage in each energy storage cluster to other distributed energy storages, and selecting the distributed energy storage with the smallest sum of distances as the cluster center of the energy storage cluster as a single sampling.

[0043] The purpose of this step is to perform a clustering iteration on the distributed energy storage based on the initial cluster center selected in step S21 and the clustering objective function defined previously.

[0044] A specific implementation process of step S23 is as follows: First, for each energy storage cluster formed in step S3 , re-evaluate and possibly update its cluster centers.

[0045] For an energy storage cluster Each distributed energy storage , calculate the distributed energy storage To the same cluster All other distributed energy storage within Here, the distance is still the difference of the clustering objective function value set in step S2.

[0046] Clustering of energy storage All distributed energy storage within After performing the above calculations, select the distributed energy storage that minimizes the total distance. As the energy storage cluster The new cluster center is calculated as follows: .

[0047] For all n energy storage clusters The above center update process is performed to obtain a new set of cluster centers and complete a sampling.

[0048] S24, repeat steps S21-S23 for several samplings, and select the comprehensive distance of all energy storage clusters and the smallest primary solution as the result of this clustering iteration, to obtain several clustered distributed energy storage clusters.

[0049] The above process will be repeated b times to select the best iteration scheme in a single clustering iteration through multiple samplings. Each repetition generates a sample, and the sum of the comprehensive distances of all energy storage clusters in each sample is calculated, that is, the sum of the distances from all energy storage to the cluster center of the energy storage cluster to which it belongs, and it is recorded as the clustering error .

[0050] Then, select the sampling with the smallest comprehensive distance and the corresponding clustering scheme as the clustering iteration result of this iteration to form the clustering iteration result. .

[0051] The flowchart of the clustering iteration process of the above steps S1-S24 is as follows: Figure 1As shown, after executing steps S1 to S24 to divide a large number of distributed energy storage units into n distributed energy storage clusters with similar performance according to their regulation performance (regulation accuracy and regulation rate), the method of the present invention then executes step S3 to use the distributed energy storage clusters for regulation.

[0052] In a preferred embodiment, step S3 includes the following sub-steps: S31. In response to the total control instruction, calculate the control instruction of each distributed energy storage cluster; When the dispatch instruction layer issues a general dispatch control instruction, the method of the present invention decomposes the general instruction and divides the regulation instruction into specific regulation instructions that should be undertaken by each distributed energy storage cluster through a quantitative allocation method of group indicators.

[0053] Specifically, step S31 calculates the control instructions of each distributed energy storage cluster by the following method: S311. Calculate the constraints of the distributed energy storage cluster; Before command allocation, the current adjustable range and operating status limitations of each distributed energy storage cluster are first determined. These constraints mainly include power constraints and capacity constraints.

[0054] Among them, the power constraint is , is the power instruction of the nth distributed energy storage cluster at time t, is the lower limit of the adjustable power of the distributed energy storage cluster. Provides an adjustable power cap for distributed energy storage clusters.

[0055] The capacity constraint is , is the comprehensive charge state of the nth distributed energy storage cluster at time t, is the lower limit of the power of the distributed energy storage cluster, It is the upper limit of the power of the distributed energy storage cluster.

[0056] S312. In response to the general regulation instruction, the regulation margin allocated to each distributed energy storage cluster is calculated according to the regulation performance of each distributed energy storage cluster and the total regulation performance of all distributed energy storage clusters.

[0057] Calculate the regulation performance of each distributed energy storage cluster , and then calculate the total regulation performance of all distributed energy storage clusters .

[0058] Calculate the regulation margin allocated to each distributed energy storage cluster .

[0059] The regulation instructions allocated to each distributed energy storage cluster are calculated while satisfying the constraints of each distributed energy storage cluster:

[0060] The regulation command will be sent to each distributed energy storage cluster until the feedback power of the distributed energy storage cluster is within the command dead zone, that is, .

[0061] S313. Regulate each distributed energy storage in the distributed energy storage cluster according to the regulation margin allocated to each distributed energy storage cluster.

[0062] After the distributed energy storage cluster receives the control instruction for the cluster, it needs to further decompose the cluster-level instruction into specific power control instructions for each distributed energy storage unit within the cluster.

[0063] The decomposition can be achieved through a variety of algorithms. In a further preferred embodiment, an equal margin control method is used to achieve the decomposition of the power control instruction.

[0064] The equal margin control method sets the capacity of the nth distributed energy storage cluster at time t to , the total capacity of the distributed energy storage cluster is , total power instruction of distributed energy storage cluster When there are m distributed energy storage units in the distributed energy storage cluster, the capacity of the i-th energy storage unit at time t is set to , the total capacity is The lower limit of the total capacity is . Where i≤m.

[0065] The positive regulation margin of the i-th distributed energy storage unit at time t is: .

[0066] The negative regulation margin of the i-th distributed energy storage unit at time t is: .

[0067] The total margin of the distributed energy storage unit of the aggregated energy storage unit at time t is , .

[0068] The power instruction allocation of the i-th distributed energy storage unit at time t is: when When positive adjustment is required: ; when When negative adjustment is required: .

[0069] Through the above steps, cluster-level control instructions are finally implemented to each distributed energy storage unit, driving them to adjust power output and thus jointly complete the cluster's control tasks.

[0070] S32. Regulate each distributed energy storage according to the control instructions.

[0071] The above process breaks down the total control instruction into cluster-level instructions that are executed separately by each cluster, and then the cluster responds accordingly and distributes them to a single distributed energy storage for execution through the equal margin method. The overall instruction decomposition process is simple, which can further make the control process of the method of this embodiment faster.

[0072] In the process of regulating the distributed energy storage according to the above method, since the performance of a single distributed energy storage will change with the operating state, if the regulation rate and regulation accuracy of the distributed energy storage change too much, it will affect the overall regulation performance. Therefore, in an improved embodiment of the present invention, the method also includes step S4, monitoring the sum of clustering errors of all distributed energy storages during the regulation process, if the change rate of the sum of clustering errors exceeds the preset value, return to step S2 for re-clustering.

[0073] Specifically, in a preferred embodiment, the preset value is that the change rate of the sum of the comprehensive distances of all energy storage clusters exceeds 50%.

[0074] When each clustering iteration is completed, step S4 will calculate the sum of the distances from all distributed energy storage to their cluster centers after this clustering. During operation, the adjustment rate and adjustment accuracy of each distributed energy storage are monitored, and the sum of the distances from all distributed energy storage to their cluster centers is calculated in real time based on these adjustment rates and adjustment accuracy.

[0075] Use this real-time data to compare with the distance sum when the last clustering was completed. If the two distance sums deviate by 50% or more, it means that the adjustment performance deviation of the current clustering is large and re-clustering is required. Return to step S2 and re-execute the clustering iteration until the distance sum deviation is less than 50%. Stop the clustering iteration and continue to control using the clustering results.

[0076] The above step S4 can continuously ensure that these distributed energy storage can achieve the preset purpose of the method of the present invention when a large number of distributed energy storage are regulated and operated, and ensure that even if the performance of the energy storage unit changes, the energy storage units with similar performance can be divided into the same cluster in real time. This is to facilitate the subsequent differentiated instruction allocation based on the characteristics of different clusters, so that the scheduling instructions can always accurately match the actual response capabilities of the cluster.

[0077] The method of the present invention uses adjustment speed and adjustment accuracy as clustering targets, and reduces the computing resource cost of the clustering algorithm by randomly selecting initial cluster centers and iterating clustering errors for multiple times, and reduces the computing resource cost of decomposing and executing control instructions by setting constraints and equal margin adjustment, thereby improving the speed of control and realizing efficient, accurate and rapid control of distributed energy storage.

[0078] Since the existing technology often ignores the huge differences in actual response speed and control accuracy of distributed energy storage units, the use of unified instructions or scheduling based only on economy leads to slow overall response and poor accuracy. The present invention obtains the actual regulation accuracy and regulation rate of each energy storage unit and clusters them based on this as the core indicator, so that energy storage units with similar performance can be divided into the same cluster. Subsequently, differentiated instructions are allocated according to the characteristics of different clusters, so that the scheduling instructions can more accurately match the actual response capabilities of the cluster, thereby effectively improving the overall speed of the entire distributed energy storage resource pool in responding to the grid scheduling instructions and the ultimate regulation accuracy, and better meeting the grid's demand for fast and accurate regulation capabilities.

[0079] An embodiment of the present invention also provides a distributed energy storage iterative clustering rapid control system, which uses the above-mentioned distributed energy storage iterative clustering rapid control method to control distributed energy storage. Specifically, the system includes a scheduling instruction layer, a clustering layer, and an optimization execution layer. The schematic diagram of the three-layer structure is as follows: Figure 2 shown.

[0080] Among them, the main function of the dispatch instruction layer is to receive the general dispatch control instructions issued by the external dispatch control platform or the dispatch control center, and pass the received general instructions to the clustering layer for processing and decomposition.

[0081] The clustering layer includes a performance data acquisition module, an iterative clustering module and a cluster instruction allocation module.

[0082] The performance data acquisition module is configured to execute step S1 of the above method to acquire or calculate the regulation accuracy of each distributed energy storage unit in the system. The iterative clustering module is configured to execute step S2 of the above method to perform clustering iteration of distributed energy storage and generate a clustered distributed energy storage cluster.

[0083] The cluster instruction allocation module is configured to execute step S3 in the above method. After clustering is completed, the module calculates the control instructions that should be allocated to each distributed energy storage cluster in response to the total control instructions received from the scheduling instruction layer.

[0084] As the underlying execution unit of the system, the optimization execution layer is responsible for receiving cluster-level control instructions from the clustering layer, and further refining and executing them to each distributed energy storage unit within the cluster.

[0085] Through the collaborative work of the above-mentioned scheduling instruction layer, clustering layer and optimization execution layer, the distributed energy storage iterative clustering rapid control system provided by the embodiment of the present invention can cluster according to the actual control performance of the distributed energy storage units, and layer, accurately allocate and execute the control instructions according to the performance margin, thereby effectively improving the overall control response speed and accuracy of the large-scale distributed energy storage cluster, while reducing the computational complexity of the central scheduling node.

[0086] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0087] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. The present invention is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A distributed energy storage iterative clustering rapid control method, characterized in that: include: S1. Obtaining the regulation accuracy and regulation rate of each distributed energy storage; S2, selecting a part from the distributed energy storage for clustering to obtain a plurality of clustered distributed energy storage clusters, wherein the clustering objective function of the clustering is a weighted sum of the regulation accuracy and the regulation rate; S3. Use the distributed energy storage cluster for regulation.

2. A distributed energy storage iterative clustering rapid control method according to claim 1, characterized in that: Also includes the steps: S4. During the control process, the total clustering error of all distributed energy storage is monitored. If the change rate of the total clustering error exceeds a preset value, return to step S2 for re-clustering.

3. A distributed energy storage iterative clustering rapid control method according to claim 1, characterized in that: The step S2 comprises: S21, selecting several from the distributed energy storage as initial clustering centers; S22, allocating the remaining distributed energy storage to the initial cluster center with the closest distance to generate a number of energy storage clusters, where the distance is the difference of the clustering objective function; S23, calculating the sum of the distances from each distributed energy storage in each of the energy storage clusters to the other distributed energy storages, and selecting the distributed energy storage with the smallest sum of the distances as the cluster center of the energy storage cluster as a primary sampling; S24, repeat steps S21-S23 for several samplings, and select the comprehensive distance of all energy storage clusters and the smallest primary solution as the result of this clustering iteration, to obtain several clustered distributed energy storage clusters.

4. A distributed energy storage iterative clustering rapid control method according to claim 1, characterized in that: The step S3 comprises: S31. In response to the total control instruction, calculate the control instruction of each distributed energy storage cluster; S32. Regulate each distributed energy storage according to the regulation instruction.

5. A distributed energy storage iterative clustering rapid control method as claimed in claim 4, characterized in that: The step S31 comprises: S311, calculating the constraints of the distributed energy storage cluster; S312, in response to the general regulation instruction, calculating the regulation margin allocated to each distributed energy storage cluster according to the regulation performance of each distributed energy storage cluster and the total regulation performance of all distributed energy storage clusters; S313 regulates each distributed energy storage therein according to the regulation margin allocated to each distributed energy storage cluster.

6. A distributed energy storage iterative clustering rapid control method as claimed in claim 4, characterized in that: When regulating each distributed energy storage according to the regulation margin allocated to each distributed energy storage cluster, an equal margin regulation method is used.

7. A distributed energy storage iterative clustering rapid control method according to claim 6, characterized in that: The equal margin control method is specifically as follows: Obtaining a positive regulation margin or a negative regulation margin of each distributed energy storage; The power of each distributed energy storage is calculated according to the proportion of the positive regulation margin or the negative regulation margin in the regulation margin of the distributed energy storage cluster.

8. A distributed energy storage iterative clustering rapid control method according to claim 1, characterized in that: The regulation accuracy of each distributed energy storage is obtained by dividing the target power instruction sent to each distributed energy storage by the actual stable output active power of the distributed energy storage.

9. A distributed energy storage iterative clustering rapid control method according to claim 1, characterized in that: The regulation rate of each distributed energy storage is calculated according to the power change rate of each distributed energy storage from the initial moment to the regulation stable moment.

10. A distributed energy storage iterative clustering rapid control system according to claim 1, characterized in that: Distributed energy storage is regulated using the distributed energy storage iterative clustering rapid regulation method as described in any one of claims 1 to 9.

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