A distributed energy storage aggregation method, device, medium, and computer program product

Abstract

The application relates to the technical field of power systems, and specifically provides a distributed energy storage aggregation method, equipment, a medium and a computer program product, the method comprising the following steps: clustering operation constraint parameters of a plurality of distributed energy storages to obtain a plurality of energy storage homogeneous clusters, and constructing corresponding benchmark isomorphic polytopes; calculating the directional similarity of an inner approximation polytope of each distributed energy storage in the cluster and an original feasible region, fusing the directional similarity with a time period weight, and obtaining a weighted directional similarity of the distributed energy storage; taking the maximization of the weighted directional similarity as an optimization target, and solving to obtain an optimal scaling factor and an optimal translation factor of the distributed energy storage; and based on the optimal scaling factor and the optimal translation factor, performing scaling and translation transformation on the benchmark isomorphic polytopes of the cluster to obtain an aggregated feasible region of the energy storage homogeneous cluster. The application fully considers the aggregation requirements of energy storage clusters in different scenarios, and can effectively support high-precision and scenario-adaptive energy storage aggregation modeling.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a distributed energy storage aggregation method, device, medium, and computer program product. Background Technology

[0002] Distributed energy storage is characterized by dispersed individual units, heterogeneous parameters, and multi-time-period coupling. Large-scale applications require unified equivalent characterization through feasible region aggregation. Existing energy storage aggregation methods primarily rely on polyhedral approximations, including box approximations, Chino polyhedral approximations, and isomorphic multicellular approximations. Box approximations neglect energy coupling constraints, resulting in significant flexibility losses; Chino polyhedral approximations, constrained by centrosymmetry, lack sufficient accuracy in approximating the feasible regions of heterogeneous energy storage systems.

[0003] Homogeneous multicells, employing a benchmark template scaling and translation for internal approximation, offer advantages in both security and computational efficiency, making them a mainstream technology for energy storage cluster aggregation. However, traditional homogeneous multicells apply equal weights to all scheduling periods during aggregation, failing to consider the priority differences in adjustment capabilities across different periods, which is incompatible with the multi-period coupled operation characteristics of energy storage. Since equal weighting leads to compressed adjustability during critical periods and redundant flexibility during non-critical periods, traditional methods cannot achieve accurate and differentiated characterization of the feasible domain for energy storage aggregation, making it difficult to meet the requirements for high-precision, adaptive, large-scale energy storage aggregation modeling. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a distributed energy storage aggregation method, device, medium and computer program product, which improves modeling accuracy and scenario adaptability by configuring time period weights.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a distributed energy storage aggregation method, comprising:

[0007] Clustering of the operational constraint parameters of multiple distributed energy storage systems yields multiple types of homogeneous energy storage clusters; a baseline isomorphic multicellular matrix is ​​constructed for each of these homogeneous energy storage clusters.

[0008] For each type of homogeneous energy storage cluster, the directional similarity between the inner approximation multiple cell of each distributed energy storage within the cluster and the original feasible region is calculated; wherein, the inner approximation multiple cell is obtained by scaling and translation transformation of the benchmark isomorphic multiple cell of the homogeneous energy storage cluster to which the distributed energy storage belongs.

[0009] Based on the scheduling scenario requirements, time period weights are configured; the time period weights are then fused with the directional similarity to obtain the weighted directional similarity of distributed energy storage.

[0010] With the goal of maximizing the weighted directional similarity, the optimal scaling factor and optimal translation factor of distributed energy storage are obtained.

[0011] Based on the optimal scaling factor and optimal translation factor of each distributed energy storage unit within the cluster, the benchmark isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster.

[0012] In some embodiments of the present invention, multiple distributed energy storage operation constraint parameters are clustered to obtain multiple types of homogeneous energy storage clusters, including:

[0013] Extract the operational constraint parameters of distributed energy storage and construct a clustering feature vector for distributed energy storage; wherein, the operational constraint parameters include: rated capacity, maximum charging power, maximum discharging power, upper and lower limits of energy safety, self-loss coefficient per unit time period, charging efficiency and discharging efficiency;

[0014] The K-means clustering algorithm is used to perform unsupervised clustering on the clustering feature vectors of each distributed energy storage system, resulting in a multi-class homogeneous energy storage cluster.

[0015] In some embodiments of the present invention, the expression for the benchmark isomorphic multicellular body is as follows:

[0016] ;

[0017] in, For the first A benchmark isomorphic multicellular structure for similar energy storage homogeneous clusters; For the first The power vector of a homogeneous energy storage cluster, where each element corresponds to the total charging and discharging power of the cluster during a scheduling period; Number of scheduling periods; For the first The constraint coefficient matrix of the feasible region for homogeneous energy storage clusters. For the first The constraint boundary vector of the feasible region of a homogeneous energy storage cluster; For the first Operational constraints of homogeneous energy storage clusters.

[0018] In some embodiments of the present invention, calculating the directional similarity between the inner approximation multiple cells of each distributed energy storage within the cluster and the original feasible region includes:

[0019] A preset number of independent random unit normal vectors are generated as sampling directions;

[0020] Calculate the ratio of the projected diameter of the inner approximation cell of each distributed energy storage unit within the cluster to the original feasible region in each sampling direction, and obtain the directional similarity between the inner approximation cell of each distributed energy storage unit and the original feasible region in each sampling direction.

[0021] In some embodiments of the present invention, time period weights are configured according to scheduling scenario requirements; the time period weights are fused with the directional similarity to obtain a weighted directional similarity of distributed energy storage, including:

[0022] Configure the time period weight of each scheduling period according to its importance in the scheduling scenario;

[0023] Based on the time period weight of each scheduling period, the directional weight of each sampling direction is obtained;

[0024] Based on the directional weights of each sampling direction, the weighted directional similarity of distributed energy storage in each sampling direction is obtained.

[0025] In some embodiments of the present invention, the formula for calculating the weighted directional similarity is as follows:

[0026] ;

[0027] in, For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Weighted directional similarity in each sampling direction; For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Orientation similarity in each sampling direction; For the first The first in a homogeneous energy storage cluster The inner approximation multicellular structure of a distributed energy storage system in the first... Projected diameter in each sampling direction; For the first The first in a homogeneous energy storage cluster The original feasible region of distributed energy storage in the first Projected diameter in each sampling direction; For the first The directional weights of each sampling direction; For the first The time period weight of each scheduling period; Number of scheduling periods; For the first The sampling direction in the first... The components of each scheduling period.

[0028] In some embodiments of the present invention, based on the optimal scaling factor and optimal translation factor of each distributed energy storage unit within the cluster, a scaling and translation transformation is performed on the baseline isomorphic multicell of the cluster to obtain the aggregated feasible region of the homogeneous energy storage cluster, including:

[0029] The optimal scaling factors of each distributed energy storage unit within the cluster are summed to obtain the total scaling factor of the cluster.

[0030] The optimal shift factors of each distributed energy storage unit within the cluster are summed to obtain the total shift factor of the cluster.

[0031] Based on the total scaling factor and total translation factor of the cluster, the benchmark isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster.

[0032] Secondly, the present invention also provides an electronic device, including: a processor, and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the above-described distributed energy storage aggregation method.

[0033] Thirdly, the present invention also provides a non-transitory machine-readable medium storing computer instructions for causing the computer to execute the above-described distributed energy storage aggregation method.

[0034] Fourthly, the present invention also provides a computer program product, including a computer program / instruction, characterized in that, when the computer program / instruction is executed by a processor, it implements the above-mentioned distributed energy storage aggregation method.

[0035] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0036] The proposed distributed energy storage aggregation method, based on the characteristic of similar energy storage systems sharing a common benchmark isomorphic multicell, transforms the traditional aggregation mode of individual convex set operations into a linear superposition operation of scaling and translation parameters. Only one linear transformation is needed to generate the feasible region for cluster aggregation. The computational complexity increases linearly with the scale of energy storage, efficiently supporting rapid aggregation modeling of large-scale distributed energy storage clusters. This invention, through a scenario-adaptive time-period weight configuration mechanism, deeply couples the time-period priority of the scheduling scenario with the directional similarity of the feasible region, driving internal approximation optimization to tilt towards key grid regulation periods. Under the premise of comparable overall computational load, it significantly improves the accuracy of feasible region representation during core scheduling periods, adapting to diverse application scenarios such as peak shaving, frequency regulation, and reserve. Attached Figure Description

[0037] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0038] Figure 1 This is a schematic flowchart of a distributed energy storage aggregation method provided in an embodiment of the present invention;

[0039] Figure 2 This is a comparative diagram illustrating the setting of differentiated time period weights in embodiments of the present invention;

[0040] Figure 3 This is a schematic diagram of the geometric mapping process of the distributed energy storage aggregation method in this embodiment of the invention;

[0041] Figure 4 This is a comparative diagram of the distributed energy storage aggregation method of this invention with other aggregation methods;

[0042] Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0044] like Figure 1 As shown in the figure, this embodiment of the invention provides a distributed energy storage aggregation method. Figure 1 This is a flowchart illustrating a distributed energy storage aggregation method. This flowchart only shows the logical sequence of the method in this embodiment. Provided there are no conflicts, different methods may be used in other possible embodiments of the invention. Figure 1 Complete the steps shown or described in the order indicated.

[0045] See Figure 1 The method of this invention specifically includes the following steps:

[0046] Step S101: Cluster the operating constraint parameters of multiple distributed energy storage systems to obtain multiple types of homogeneous energy storage clusters; construct the benchmark isomorphic multicellular matrix of each homogeneous energy storage cluster.

[0047] In this embodiment of the invention, the basic modeling of distributed energy storage is as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] in, for The charging and discharging power of distributed energy storage during specific time periods; This represents the maximum charging and discharging power of distributed energy storage. This refers to the minimum charge and discharge power for distributed energy storage. for The remaining electricity of distributed energy storage during specific time periods; The upper limit of the safe power capacity of distributed energy storage; This represents the lower limit of safe power capacity for distributed energy storage. This represents the self-loss coefficient of distributed energy storage. The charging and discharging efficiency of distributed energy storage.

[0052] In this embodiment of the invention, clustering the operational constraint parameters of multiple distributed energy storage systems to obtain multiple types of homogeneous energy storage clusters may include:

[0053] The operational constraint parameters of distributed energy storage are extracted to construct a clustering feature vector for distributed energy storage. The operational constraint parameters include: rated capacity, maximum charging power, maximum discharging power, upper and lower limits of energy safety, self-loss coefficient per unit time period, charging efficiency, and discharging efficiency. The K-means clustering algorithm is used to perform unsupervised clustering on the clustering feature vectors of each distributed energy storage system to obtain multiple homogeneous energy storage clusters.

[0054] Isomorphic multicellular structures are convex polyhedra with uniform geometric structures and constraints.

[0055] The expression for the baseline isomorphic multicellular body in this embodiment of the invention is as follows:

[0056] ;

[0057] in, For the first A benchmark isomorphic multicellular structure for similar energy storage homogeneous clusters; For the first The power vector of a homogeneous energy storage cluster, where each element corresponds to the total charging and discharging power of the cluster during a scheduling period; Number of scheduling periods; For the first The constraint coefficient matrix of the feasible region for homogeneous energy storage clusters. For the first The constraint boundary vector of the feasible region of a homogeneous energy storage cluster; For the first Operational constraints of homogeneous energy storage clusters.

[0058] For each type of homogeneous energy storage cluster, the benchmark isomorphic multicellular structure is obtained by averaging the parameters of each resource within the cluster. Taking distributed energy storage aggregation during a scheduling period as an example, its and The form is:

[0059] ;

[0060] in, It is the identity matrix; , and This is the energy constraint coefficient matrix related to charge and discharge efficiency. , and The specific form is as follows:

[0061] ;

[0062] ;

[0063] in, The duration of the scheduling period; This represents the initial energy level for distributed energy storage. The correlation coefficient; .

[0064] Step S102: For each type of homogeneous energy storage cluster, calculate the directional similarity between the inner approximation multiple cell of each distributed energy storage within the cluster and the original feasible domain.

[0065] Among them, the internal approximation multicell is obtained by scaling and translation transformation of the benchmark isomorphic multicell of the energy storage homogeneous cluster to which its distributed energy storage belongs.

[0066] The expression for the internal approximation multicellular form of distributed energy storage is as follows:

[0067] ;

[0068] in, For the first The first in a homogeneous energy storage cluster An internal approximation multiple cell of a distributed energy storage system; For the first The first in a homogeneous energy storage cluster The original feasible domain of distributed energy storage; For the first The first in a homogeneous energy storage cluster Scaling factor for distributed energy storage; For the first The first in a homogeneous energy storage cluster A translation factor for distributed energy storage; For the first The first in a homogeneous energy storage cluster The charging and discharging power of distributed energy storage.

[0069] Calculate the directional similarity between the inner approximation multiple cells of each distributed energy storage unit within the cluster and the original feasible region, specifically including:

[0070] Generate a preset number of independent random unit normal vectors as sampling directions; calculate the ratio of the projection diameter of the inner approximation cell of each distributed energy storage in the cluster to the original feasible region in each sampling direction, and obtain the directional similarity between the inner approximation cell of each distributed energy storage and the original feasible region in each sampling direction.

[0071] In this embodiment of the invention, multiple independent random unit normal vectors are constructed, and the directional similarity between the inner approximation multiple cell of distributed energy storage and the original feasible region in each sampling direction is defined as follows:

[0072] ;

[0073] in, For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Orientation similarity in each sampling direction; For the first The first in a homogeneous energy storage cluster The inner approximation multicellular structure of a distributed energy storage system in the first... Projected diameter in each sampling direction; For the first The first in a homogeneous energy storage cluster The original feasible region of distributed energy storage in the first Projected diameter in each sampling direction; , A higher value indicates a higher similarity between the inner approximation multicell and the original feasible domain.

[0074] and The moderating potential boundary of the inner approximation multiple cell and the original feasible region of distributed energy storage in a specific direction is quantified, and its value can be obtained by solving a linear programming problem:

[0075] ;

[0076] ;

[0077] in, It is a power vector; For the first One sampling direction.

[0078] Step S103: Configure time period weights according to scheduling scenario requirements; integrate time period weights with directional similarity to obtain weighted directional similarity of distributed energy storage.

[0079] In this embodiment of the invention, step S103 may include:

[0080] Based on the importance of each scheduling period in the scheduling scenario, the time period weight of each scheduling period is configured; based on the time period weight of each scheduling period, the directional weight of each sampling direction is obtained; based on the directional weight of each sampling direction, the weighted directional similarity of distributed energy storage in each sampling direction is obtained.

[0081] The formula for calculating weighted directional similarity is as follows:

[0082] ;

[0083] in, For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Weighted directional similarity in each sampling direction; For the first The directional weights of each sampling direction; For the first The time period weight of each scheduling period; Number of scheduling periods; For the first The sampling direction in the first... The components of each scheduling period.

[0084] Step S104: With the goal of maximizing the weighted directional similarity, solve for the optimal scaling factor and optimal translation factor of distributed energy storage.

[0085] The embodiments of the present invention take maximizing the weighted average similarity as the optimization objective and construct a maximum internal approximation optimization model for distributed energy storage to ensure that the internal approximation multicell fits the original feasible domain to the maximum extent during the core period.

[0086] No. The first in a homogeneous energy storage cluster The maximum internal approximation optimization model for distributed energy storage is as follows:

[0087] .

[0088] Based on optimization theory, a commercial optimization solver is invoked to obtain the optimal scaling factor and optimal translation factor for distributed energy storage.

[0089] Step S105: Based on the optimal scaling factor and optimal translation factor of each distributed energy storage in the cluster, perform scaling and translation transformations on the baseline isomorphic multicell of the cluster to obtain the aggregated feasible region of the homogeneous energy storage cluster.

[0090] In this embodiment of the invention, step S105 may include:

[0091] The optimal scaling factors of each distributed energy storage unit within the cluster are summed to obtain the total scaling factor of the cluster; the optimal translation factors of each distributed energy storage unit within the cluster are summed to obtain the total translation factor of the cluster; based on the total scaling factor and the total translation factor of the cluster, the baseline isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster.

[0092] Since each distributed energy storage unit within the same cluster is obtained by transforming a homogeneous multicellular object based on the same benchmark, aggregation can be achieved by linearly superimposing scaling and translation factors.

[0093] ;

[0094] in, For the first Aggregational feasible region of homogeneous energy storage clusters; For the first The number of distributed energy storage units within a homogeneous energy storage cluster; No. The first in a homogeneous energy storage cluster The optimal scaling factor for distributed energy storage; For the first The first in a homogeneous energy storage cluster The optimal translation factor for distributed energy storage.

[0095] The overall control constraint of homogeneous energy storage clusters adopts the form of "joint constraint of feasible regions of small clusters" to achieve coordinated scheduling of all energy storage resources. Its unified constraint expression is as follows:

[0096] ;

[0097] ; ; ;

[0098] in, It is a block diagonal matrix, and the diagonal elements are the constraint coefficient matrix of the aggregate feasible region of various types of homogeneous energy storage clusters; The number of homogeneous energy storage clusters; The aggregated total power vector is composed of aggregated power sub-vectors from various types of homogeneous energy storage clusters. Stacked together, serving as the overall control variable for upper-level scheduling; The aggregated total boundary vector is composed of aggregated boundary sub-vectors of various homogeneous energy storage clusters. It is formed by transformation and stacking. This is the overall scaling factor for each homogeneous energy storage cluster; The total translation factor of each homogeneous energy storage cluster, together with the other factor, determines the boundary location and size of the feasible domain for aggregation of this type of cluster.

[0099] Output the aggregated feasible region constraint coefficient matrix for various types of homogeneous energy storage clusters. With boundary vector After obtaining these two sets of parameters, the upper-level scheduler aggregates the total power. To optimize the variables, we substitute them with uniform constraints. This allows for the solution of the optimal scheduling scheme while satisfying all cluster operation constraints, thereby achieving coordinated scheduling of energy storage resources.

[0100] Figure 2 This diagram illustrates the weighting of distributed energy storage across two differentiated time periods. The horizontal axis represents the active power output P1 of the distributed energy storage in time period 1, and the vertical axis represents the active power output P2 of the distributed energy storage in time period 2. The gray area represents the baseline isomorphic multicell, and the optimal inner approximation of the original feasible region is obtained by scaling and translating it.

[0101] When the area obtained is the inner approximation of the maximum area of ​​the original feasible region; At that time, the optimal inner approximation area decreased, but the active power limit of time period 1 increased, releasing the flexible adjustment capability of time period 1. Much larger At that time, the inner approximation area decreases again, but the active power output in time period 1 reaches the maximum value of the original feasible region. Therefore, introducing differentiated time period weights can effectively release the flexibility of the target time period.

[0102] In this embodiment of the invention, a set of typical distributed energy storage device parameters is set, and three sub-type devices are formed by adding random biases. The specific parameters of the typical device are shown in Table 1.

[0103] Table 1 Typical Equipment Parameters for Distributed Energy Storage

[0104]

[0105] Figure 3This illustrates the complete geometric mapping process from the baseline isomorphic multicell to the adaptation of distributed energy storage units, and then to overall aggregation. The gray area in the top figure represents the baseline isomorphic multicell drawn based on the typical energy storage characteristics in Table 1. The benchmark isomorphic multicellular structure, acting as the "gene" of the aggregate shape, has its boundaries determined by power and energy constraints. The middle section illustrates the internal approximation process of distributed energy storage. Due to significant differences in the physical parameters (such as initial charge and efficiency) of the three distributed energy storage systems ES1, ES2, and ES3, their original feasible regions... , and They differ in both spatial location and geometric dimensions. By optimizing the scaling and translation factors, the baseline isomorphic multicell achieves accurate internal approximation of the feasible domains of each heterogeneous monomer while maintaining its shape and proportions. Figure 2 The bottom shows the final theoretical polymerization boundary and the polymerization results of isomorphic multicellular structures. The feasible region after polymerization. isomorphic multicellular structures with reference standard Maintaining strict geometric isomorphism, the feasible regions of different devices can be superimposed by simply superimposing scaling factors and translation factors, which can greatly simplify the calling cost of upper-level scheduling models for large-scale cluster constraints.

[0106] To further quantify the accuracy advantage of this invention when processing large-scale resources, 40 distributed energy storage devices were randomly generated according to the energy storage subclass. The Box approximation method, the Zonotope approximation method, and the distributed energy storage aggregation method proposed in this embodiment were used for aggregation and comparison. The average weighted similarity of each sampling direction is shown below. Figure 4 As shown.

[0107] See Figure 4 The Box method, by only considering the upper and lower power limits for decoupling and ignoring the energy coupling across time periods, suffers from a significant loss of flexibility. While the Zonotope method can describe certain coupling characteristics, its inherent centrosymmetry makes it difficult to achieve deep integration when facing asymmetric feasible regions caused by initial charge or efficiency. In contrast, the method of this invention, through scaling and translation of a baseline polycell, can accurately capture the geometric contours of heterogeneous devices. Under 24-time-period aggregation, its aggregation accuracy is improved by approximately 20.1% compared to the Zonotope method, greatly unlocking the adjustment potential of distributed resources.

[0108] Embodiments of the present invention also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the spreadsheet data batch processing method of embodiments of the present invention.

[0109] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the spreadsheet data batch processing method of embodiments of the present invention.

[0110] An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the spreadsheet data batch processing method of the present invention.

[0111] refer to Figure 5 The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0112] like Figure 5 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0113] Multiple components in the electronic device are connected to I / O interface 105, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information into the electronic device. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, disks and optical discs. Communication unit 109 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0114] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 102 and / or communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0115] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0117] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0118] 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, stored data, displayed data, etc.) involved in the embodiments of this invention are subject to strict compliance with relevant laws, regulations, and regulatory requirements in their collection, storage, use, processing, transmission, provision, and disclosure, and adhere to the principles of legality, legitimacy, necessity, and good faith. The acquisition of relevant information and data is premised on the user's explicit consent or other legitimate reasons, and a clear and convenient authorization management approach is provided to the user, allowing the user to independently choose to consent, withdraw consent, or refuse to provide relevant information. For functions that rely on user information, if the user does not authorize or withdraws authorization, the corresponding technical function cannot be implemented, and the technical solution of this invention is not applicable in this scenario.

[0119] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0120] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0121] The above embodiments merely illustrate several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A distributed energy storage aggregation method, characterized in that, include: Clustering of the operational constraints of multiple distributed energy storage systems yields multiple types of homogeneous energy storage clusters. Construct a benchmark isomorphic multicellular structure for each of the aforementioned energy storage homogeneous clusters; For each type of homogeneous energy storage cluster, the directional similarity between the inner approximation multiple cell of each distributed energy storage within the cluster and the original feasible region is calculated; wherein, the inner approximation multiple cell is obtained by scaling and translation transformation of the benchmark isomorphic multiple cell of the homogeneous energy storage cluster to which the distributed energy storage belongs. Based on the scheduling scenario requirements, time period weights are configured; the time period weights are then fused with the directional similarity to obtain the weighted directional similarity of distributed energy storage. With the goal of maximizing the weighted directional similarity, the optimal scaling factor and optimal translation factor of distributed energy storage are obtained. Based on the optimal scaling factor and optimal translation factor of each distributed energy storage unit within the cluster, the benchmark isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster.

2. The distributed energy storage aggregation method according to claim 1, characterized in that, Clustering the operational constraints of multiple distributed energy storage systems yields several types of homogeneous energy storage clusters, including: Extract the operational constraint parameters of distributed energy storage and construct a clustering feature vector for distributed energy storage; wherein, the operational constraint parameters include: rated capacity, maximum charging power, maximum discharging power, upper and lower limits of energy safety, self-loss coefficient per unit time period, charging efficiency and discharging efficiency; The K-means clustering algorithm is used to perform unsupervised clustering on the clustering feature vectors of each distributed energy storage system, resulting in a multi-class homogeneous energy storage cluster.

3. The distributed energy storage aggregation method according to claim 2, characterized in that, The expression for the benchmark isomorphic multicellular body is as follows: ； in, For the first A benchmark isomorphic multicellular structure for similar energy storage homogeneous clusters; For the first The power vector of a homogeneous energy storage cluster, where each element corresponds to the total charging and discharging power of the cluster during a scheduling period; Number of scheduling periods; For the first The constraint coefficient matrix of the feasible region for homogeneous energy storage clusters. For the first The constraint boundary vector of the feasible region of a homogeneous energy storage cluster; For the first Operational constraints of homogeneous energy storage clusters.

4. The distributed energy storage aggregation method according to claim 1, characterized in that, Calculate the directional similarity between the inner approximation multiple cells of each distributed energy storage unit within the cluster and the original feasible region, including: A preset number of independent random unit normal vectors are generated as sampling directions; Calculate the ratio of the projected diameter of the inner approximation cell of each distributed energy storage unit within the cluster to the original feasible region in each sampling direction, and obtain the directional similarity between the inner approximation cell of each distributed energy storage unit and the original feasible region in each sampling direction.

5. The distributed energy storage aggregation method according to claim 4, characterized in that, Based on the scheduling scenario requirements, time period weights are configured; the time period weights are then fused with the directional similarity to obtain the weighted directional similarity of distributed energy storage, including: Configure the time period weight of each scheduling period according to its importance in the scheduling scenario; Based on the time period weight of each scheduling period, the directional weight of each sampling direction is obtained; Based on the directional weights of each sampling direction, the weighted directional similarity of distributed energy storage in each sampling direction is obtained.

6. The distributed energy storage aggregation method according to claim 5, characterized in that, The formula for calculating the weighted directional similarity is as follows: ； in, For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Weighted directional similarity in each sampling direction; For the first The first in a homogeneous energy storage cluster The distributed energy storage in the first Orientation similarity in each sampling direction; For the first The first in a homogeneous energy storage cluster The inner approximation multicellular structure of a distributed energy storage system in the first... Projected diameter in each sampling direction; For the first The first in a homogeneous energy storage cluster The original feasible region of distributed energy storage in the first Projected diameter in each sampling direction; For the first The directional weights of each sampling direction; For the first The time period weight of each scheduling period; Number of scheduling periods; For the first The sampling direction in the first... The components of each scheduling period.

7. The distributed energy storage aggregation method according to claim 1, characterized in that, Based on the optimal scaling and translation factors of each distributed energy storage unit within the cluster, the baseline isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster, including: The optimal scaling factors of each distributed energy storage unit within the cluster are summed to obtain the total scaling factor of the cluster. The optimal shift factors of each distributed energy storage unit within the cluster are summed to obtain the total shift factor of the cluster. Based on the total scaling factor and total translation factor of the cluster, the benchmark isomorphic multicell of the cluster is scaled and translated to obtain the aggregated feasible region of the homogeneous energy storage cluster.

8. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the distributed energy storage aggregation method according to any one of claims 1 to 7.

9. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the distributed energy storage aggregation method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the distributed energy storage aggregation method according to any one of claims 1 to 7.

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