Two-stage aggregation method and system for heterogeneous distributed controllable and adjustable resources of large power grid

Abstract

The invention belongs to the technical field of power grid dispatching, and particularly relates to a two-stage aggregation method and system for heterogeneous distributed controllable and adjustable resources of a large power grid. The method comprises the following steps: firstly, determining each distributed resource belonging to the same node, and constructing a uniform feature vector containing a resource adjustment capability parameter and a system load state parameter; then, according to the unified feature vector, clustering the resources according to a scheduling period by adopting a self-adaptive clustering algorithm based on Euclidean distance, automatically determining the number of clustering categories, and calculating aggregation adjustment capability parameters of each type of resources; and finally, taking the adjusted power grid load curve closest to the peak clipping and valley filling target curve as a target, establishing an optimization model considering the node adjustment amount constraint and the network branch constraint for solving, and obtaining an optimal scheduling scheme. The method solves the problem that the existing resource aggregation method does not fully consider the system load state and depends on manual setting of the clustering number, and improves the accuracy and scheduling efficiency of resource aggregation.

Description

Technical Field

[0001] This invention belongs to the field of power grid dispatching technology, specifically relating to a two-stage aggregation method and system for heterogeneous distributed controllable and adjustable resources in a large power grid. Background Technology

[0002] With the increasing proportion of renewable energy generation and the widespread integration of diverse loads, the quantity and types of distributed controllable and adjustable resources in the power grid are growing rapidly, including distributed energy storage, electric vehicles, and adjustable loads. These resources are diverse, and how to efficiently and accurately aggregate these massive and heterogeneous resources to enable them to participate in the grid's dispatch and operation, especially to achieve goals such as peak shaving and valley filling and smoothing load curves, has become a research hotspot and key technical challenge in the field of smart grids.

[0003] Therefore, there is an urgent need for a new aggregation method that can dynamically integrate resource characteristics and system state information and automatically determine the optimal aggregation classification to improve the accuracy and effectiveness of the regulation of heterogeneous distributed resource clusters. Summary of the Invention

[0004] The purpose of this invention is to provide a two-stage aggregation method and system for heterogeneous distributed controllable and adjustable resources in a large power grid, so as to solve the problem of aggregation of distributed controllable and adjustable resources in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, wherein the large power grid includes various nodes; The methods include: Identify the distributed controllable and adjustable resources belonging to the same node, and determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; Based on the unified feature vector, each distributed controllable and adjustable resource is clustered according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; Determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; among which, the active power adjustment amount is used to adjust the load curve of the large power grid; Determine the target curve for peak shaving and valley filling of the large power grid load curve. Establish an objective function with the goal of making the large power grid load curve after adjustment through the active power adjustment of each node as close as possible to the target curve, and determine the constraints of the objective function. The objective function is solved based on the constraints to obtain the adjustment scheme.

[0006] Furthermore, physical parameters characterizing resource regulation capabilities include maximum up-regulation capacity, maximum down-regulation capacity, ramp rate, response time, minimum energy state, and maximum energy state. The parameters characterizing the system load state include the load forecast value, the maximum load forecast value, and the minimum load forecast value; The unified feature vector also includes the magnitude of the load forecast value relative to the minimum load forecast value, and the magnitude of the load forecast value relative to the maximum load forecast value.

[0007] Furthermore, an objective function is established with the goal of making the load curve of the large power grid after adjustment through the active power adjustment at each node as close as possible to the target curve. The objective function is as follows:

[0008] in, For the first Load forecast values ​​for the scheduling period For the first Load target value for the scheduling period For the first Scheduling time period nodes The active power adjustment is T, where T is the total number of scheduling periods and N is the total number of nodes.

[0009] Furthermore, the constraints of the objective function are determined, including load node adjustment constraints and network branch constraints.

[0010] Furthermore, the load node adjustment constraint is as follows:

[0011] .

[0012] Furthermore, the network branch constraints are as follows:

[0013] in, For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Scheduling Node A vector composed of active power predictions. For the first Scheduling Node Active power adjustment The vector formed; branch road The susceptance; For the first Active power injection vector of nodes during the scheduling period; branch road The active power limit.

[0014] Furthermore, based on the unified feature vector, each distributed controllable and adjustable resource is clustered according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period, including: For a given scheduling period, determine the unified feature vector corresponding to each distributed controllable and adjustable resource under the current scheduling period; construct a sample set based on the unified feature vector of each distributed controllable and adjustable resource; The Euclidean distance between samples is calculated based on the sample set, and the samples are clustered based on an adaptive clustering algorithm to obtain the categories of distributed controllable and adjustable resources in the current scheduling period. The adaptive clustering algorithm automatically determines the final number of clusters and each category by iteratively adjusting the distance threshold and judging whether the change in the number of clusters converges.

[0015] In a second aspect, the present invention provides a two-stage aggregation system for heterogeneous distributed controllable and adjustable resources in a large power grid, wherein the large power grid includes nodes; the device includes: The first processing module is used to determine each distributed controllable and adjustable resource belonging to the same node, and to determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; The second processing module is used to cluster each distributed controllable and adjustable resource according to the scheduling period based on the unified feature vector, so as to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; The third processing module is used to determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; and to determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; wherein, the active power adjustment amount is used to adjust the load curve of the large power grid. The fourth processing module is used to determine the target curve for peak shaving and valley filling of the power grid load curve. The objective function is established with the goal of making the power grid load curve after adjustment by the active power adjustment of each node as close as possible to the target curve, and the constraints of the objective function are determined. The fifth processing module is used to solve the objective function based on the constraints and obtain the adjustment scheme.

[0016] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the two-stage aggregation method for heterogeneous distributed controllable and adjustable resources of a large power grid as described above.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid as described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This solution provides a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid. Compared with existing technologies, this solution constructs a unified feature vector containing resource regulation capability parameters and system load state parameters, and adaptively determines the number of clusters based on sample distance, thereby achieving accurate aggregation and optimized scheduling of distributed controllable and adjustable resources. It overcomes the problems of existing technologies that do not fully consider system load state and rely on manually setting the number of clusters, thus improving the accuracy and adaptability of resource aggregation.

[0019] By clearly defining maximum up-adjustment capacity, maximum down-adjustment capacity, ramp rate, response time, minimum and maximum energy state as resource regulation capability parameters, and load forecast, maximum and minimum load forecast as system load state parameters, and introducing the magnitude of load forecast relative to minimum and maximum load forecast, comprehensive and quantitative characteristic indicators are provided for resource clustering, effectively supporting the accuracy of peak shaving and valley filling.

[0020] By employing an adaptive clustering algorithm that iteratively adjusts the distance threshold and judges the convergence of the number of clusters, the number of clusters is automatically determined, avoiding manual intervention, improving the objectivity and accuracy of the clustering results, and better reflecting the true grouping characteristics of resources. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of a two-stage aggregation system for heterogeneous distributed controllable and adjustable resources in a large power grid, according to an embodiment of the present invention. Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0023] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0024] Example 1 It should be noted that the large power grid referred to in this plan consists of nodes of the power grid, and each node is distinguished by a bus. Under a node, there are various distributed, controllable and adjustable resources, such as new energy power generation and thermal power generation.

[0025] like Figure 1 As shown, this invention provides a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, where the power grid includes various nodes; the method includes: S1. Determine each distributed controllable and adjustable resource belonging to the same node, and determine the unified feature vector of the distributed controllable and adjustable resources; whereby the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; S2. Based on the unified feature vector, cluster each distributed controllable and adjustable resource according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters obtained is determined based on the sample distance; S3. Determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; among which, the active power adjustment amount is used to adjust the load curve of the large power grid; S4. Determine the target curve for peak shaving and valley filling of the power grid load curve. Establish the objective function with the goal of making the power grid load curve after adjustment through the active power adjustment of each node as close to the target curve as possible, and determine the constraints of the objective function. S5. Solve the objective function based on the constraints to obtain the adjustment scheme.

[0026] The method of this invention is a two-stage approach. In the first stage, the distributed controllable and adjustable resources under the nodes are clustered using an adaptive clustering method based on Euclidean distance to obtain the aggregation of distributed controllable and adjustable resources in each scheduling period, thereby significantly reducing the dimensionality of the distributed controllable and adjustable resources. In the second stage, an objective function is established with the goal of making the load curve of the large power grid after adjustment through the active power adjustment of each node as close as possible to the target curve. Considering the load node adjustment constraints and network branch constraints, cross-node optimization is performed on the aggregation of distributed controllable and adjustable resources under each node, thereby realizing the optimization of the distributed controllable and adjustable resources of the entire network in each scheduling period.

[0027] In one embodiment, the present invention provides a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, comprising the following steps: S100. Determine each distributed controllable and adjustable resource belonging to the same node, and determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status.

[0028] Specifically, the physical parameters characterizing resource regulation capacity in this scheme include maximum up-adjustment capacity, maximum down-adjustment capacity, ramp rate, response time, minimum energy state value, and maximum energy state value; the parameters characterizing system load status include load forecast value, maximum load forecast value, and minimum load forecast value; the unified feature vector also includes the magnitude of the load forecast value relative to the minimum load forecast value, and the magnitude of the load forecast value relative to the maximum load forecast value.

[0029] As an example, the unified feature vector of the distributed, controllable, and adjustable resources belonging to the same node is represented as: (1) in, For nodes The following is the first Distributed, controllable, and adjustable resources in the first... A unified feature vector for the scheduling period; For nodes The following is the first Distributed, controllable, and adjustable resources in the first... Maximum capacity increase during the scheduling period For nodes The following is the first Distributed, controllable, and adjustable resources in the first... Maximum capacity reduction during the scheduling period For nodes The following is the first Distributed, controllable, and adjustable resources in the first... The ramp rate during the scheduling period, For nodes The following is the first Distributed, controllable, and adjustable resources in the first... Response time during the scheduling period For nodes The following is the first Distributed, controllable, and adjustable resources in the first... Minimum energy state during the scheduling period For nodes The following is the first Distributed, controllable, and adjustable resources in the first... Maximum energy state during the scheduling period. For the first Load forecast values ​​for the scheduling period This represents the maximum load forecast. This represents the minimum load forecast value. Indicates the first The magnitude of the load forecast value during the scheduling period relative to the maximum load forecast value. Indicates the first The magnitude of the load forecast value during the scheduling period relative to the minimum load value is equivalent to adding information about the peak and valley load of the system during that scheduling period to a unified feature vector.

[0030] The above steps establish a unified feature vector for the distributed, controllable, and adjustable resources of the entire network at each scheduling period, including information such as upscaling capacity, downscaling capacity, and ramp-up rate. The unified feature vector also incorporates the ratio of the system load to the system load peak-to-valley value corresponding to the scheduling period. This includes physical parameters characterizing resource adjustment capabilities, parameters characterizing system load status, and state parameters reflecting its correlation with system load peaks and valleys (load ratio). , This can accurately characterize the adjustment potential of resources during the scheduling period and their matching degree with system requirements, thereby improving the accuracy of peak shaving and valley filling of distributed controllable and adjustable resources.

[0031] S200. Based on the unified feature vector, cluster each distributed controllable and adjustable resource according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance.

[0032] The unified feature vector of all distributed, controllable, and adjustable resources under a certain node across all scheduling periods is used as the total cluster sample: (2) Where M is the total number of clustered samples of the current node, which is the product of the total number of distributed controllable and adjustable resources under the current node and the total number of scheduling periods. u k This represents the k-th sample in the total cluster.

[0033] Formula 2 includes a unified feature vector for all distributed, controllable, and adjustable resources across all scheduling periods. Based on this, the total cluster samples are divided according to the scheduling periods to obtain sample sets corresponding to each scheduling period. During clustering, a sample set for a specific scheduling period is selected, and for any two samples in the sample set, the Euclidean distance between them is calculated.

[0034] The formula for calculating Euclidean distance is: (3) in, For the sample The One element, For the sample The One element, The number of elements in the feature vector of the sample is given by formula (1). The value is 8.

[0035] Perform adaptive clustering according to the following steps: (1) Set an initial value D_lim for the Euclidean distance between samples as the distance threshold; (2) Set the number of clusters calculated in the current iteration to G=0, and the number of clusters in the previous iteration to G_last=0; (3) Select the first sample as the initial cluster center; (4) Calculate the Euclidean distance between other samples in the sample set and the initial cluster center, and cluster samples whose Euclidean distance is less than the distance threshold D_lim into one class, and G=G+1; (5) Select the sample with the largest distance as the next cluster center; (6) Determine whether all samples in the sample set have participated in clustering. If not, return (4); otherwise, proceed to (7). (7) Determine whether the difference between G_last and G is less than the preset value. If it is less, the clustering ends. Otherwise, let D_lim=D_lim*1.1, G_last=G, G=0, and return to (3). (8) Based on the above steps, the number of clusters G and the class to which each sample belongs in the current scheduling period are obtained.

[0036] In the above scheme, the distance threshold D_lim is used to determine whether two samples are similar enough to be classified into the same class. If the Euclidean distance between two samples is less than the distance threshold D_lim, they are considered to belong to the same cluster. The distance threshold is adaptively adjusted during the algorithm. After each complete clustering process, the value of G represents the number of classes obtained based on the current D_lim threshold. The cluster count G_last from the previous iteration is compared with the current G to determine whether the clustering results have stabilized.

[0037] The aforementioned adaptive clustering method, based on Euclidean distance adaptive clustering, correlates the distance between clustered samples with the final number of clusters. By automatically adjusting the distance threshold D_lim, it seeks a natural clustering result that makes the samples within a cluster sufficiently similar and the samples between clusters sufficiently different. The resulting clustering result is closest to the true state, thus avoiding the subjectivity and inaccuracy of manually setting the number of clusters K in advance.

[0038] In one embodiment, this scheme further explains and illustrates the adaptive clustering method: First, based on experience, an initial, relatively small distance threshold D_lim is set, with G_last=0 in the first iteration. The first sample is used as the initial cluster center. All other samples are iterated through, and their distances to the initial cluster centers are calculated. Samples with distances less than the distance threshold D_lim are assigned to this cluster. Because the initial value of D_lim is small, only very similar samples will be clustered together, resulting in multiple clusters in this initial clustering.

[0039] Then, the sample furthest from the current cluster center in the previous cycle is selected as the new cluster center, and the clustering process is repeated until all samples are classified.

[0040] Next, the number of clusters G in this iteration is compared with the number of clusters G_last in the previous iteration. If the difference between G and G_last is less than a preset value, it means that even if the threshold is increased further, the number of clusters will no longer change significantly, and the clustering result has stabilized. The algorithm ends, and the current number of clusters G and the classification result are output. If the difference between G and G_last is greater than a preset value, it means that the current distance threshold D_lim is still too small, resulting in overly fine classification. Therefore, the threshold D_lim is increased (multiplied by 1.1 in this scheme), and the entire clustering process is restarted with the new, larger distance threshold. It should be noted that increasing the threshold means relaxing the similarity criteria; similar samples that were originally divided into multiple classes will now be merged into fewer classes. Therefore, the number of clusters G obtained in the next iteration will decrease.

[0041] Finally, the algorithm converges when the number of clusters remains essentially unchanged after two consecutive iterations. The final number of clusters G and the category to which each sample belongs are output.

[0042] The advantage of the adaptive clustering method proposed in this scheme is that it can transform the difficult question of "how many clusters should be formed" into an operable process of "adjusting the distance threshold until the result is stable". This realizes the automatic determination of the number of clusters, making the aggregation results more reflective of the inherent and real group characteristics of distributed resources, and providing a more accurate basis for subsequent power grid dispatch optimization.

[0043] The aforementioned adaptive clustering based on Euclidean distance correlates the distance between clustered samples with the final number of clusters, resulting in clustering results that most closely approximate the true state and avoid the inaccuracy caused by manually setting the number of clusters. Distributed, controllable, and adjustable resources are aggregated according to scheduling periods, and peak and valley information of the system load is added to a unified feature vector, enabling the optimized aggregation of distributed, controllable, and adjustable resources to better support peak shaving and valley filling.

[0044] S300: Determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; wherein, the active power adjustment amount is used to adjust the load curve of the large power grid.

[0045] After clustering, the first Adjusting response time of classes for: (4) in, For the first The number of samples in the class For the first The response time of the i-th distributed, controllable, and adjustable resource in the class.

[0046] No. Increase capacity of class aggregation for: (5) in, For the first The maximum upsizing capacity of the i-th distributed controllable and adjustable resource in the class.

[0047] No. Class aggregation capacity reduction for: (6) in, For the first The maximum downsizing capacity of the i-th distributed controllable and adjustable resource in the class.

[0048] S400. Determine the target curve for peak shaving and valley filling of the power grid load curve. Establish an objective function with the objective of the power grid load curve after adjustment through the active power adjustment of each node being closest to the target curve, and determine the constraints of the objective function.

[0049] Specifically, the objective function is as follows: (7) in, For the first Load forecast values ​​for the scheduling period For the first Load target value for the scheduling period For the first Scheduling time period nodes The active power adjustment is T, where T is the total number of scheduling periods and N is the total number of nodes.

[0050] Specifically, the constraints of the objective function are determined, including load node adjustment constraints and network branch constraints.

[0051] As an example, the load node adjustment constraint is: (8) (9) As an example, the network branch constraint is: (10) in, For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Scheduling Node A vector composed of active power predictions. For the first Scheduling Node Active power adjustment The vector formed; branch road The susceptance; For the first Active power injection vector of nodes during the scheduling period; branch road The active power limit.

[0052] S500. Solve the objective function based on the constraints to obtain the adjustment scheme.

[0053] Specifically, this solution can be based on constraints and a mathematical solver can be called to obtain the optimal adjustment scheme.

[0054] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a two-stage aggregation system for heterogeneous distributed controllable and adjustable resources in a large power grid, comprising: The first processing module is used to determine each distributed controllable and adjustable resource belonging to the same node, and to determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; The second processing module is used to cluster each distributed controllable and adjustable resource according to the scheduling period based on the unified feature vector, so as to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; The third processing module is used to determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; and to determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; wherein, the active power adjustment amount is used to adjust the load curve of the large power grid. The fourth processing module is used to determine the target curve for peak shaving and valley filling of the power grid load curve. The objective function is established with the goal of making the power grid load curve after adjustment by the active power adjustment of each node as close as possible to the target curve, and the constraints of the objective function are determined. The fifth processing module is used to solve the objective function based on the constraints and obtain the adjustment scheme.

[0055] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on at least one processor 102, and at least one communication bus 104.

[0056] The memory 101 can be used to store computer program 103. The processor 102 implements the steps of the two-stage aggregation method for heterogeneous distributed controllable and adjustable resources of a large power grid in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0057] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0058] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0059] The memory 101 in the electronic device 100 stores multiple instructions to implement a two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, and the processor 102 can execute multiple instructions to achieve the following: Identify the distributed controllable and adjustable resources belonging to the same node, and determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; Based on the unified feature vector, each distributed controllable and adjustable resource is clustered according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; Determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; among which, the active power adjustment amount is used to adjust the load curve of the large power grid; Determine the target curve for peak shaving and valley filling of the large power grid load curve. Establish an objective function with the goal of making the large power grid load curve after adjustment through the active power adjustment of each node as close as possible to the target curve, and determine the constraints of the objective function. The objective function is solved based on the constraints to obtain the adjustment scheme.

[0060] Example 4 If the modules / units integrated in the electronic device 100 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, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid, characterized in that, A large power grid includes various nodes; methods include: Identify the distributed controllable and adjustable resources belonging to the same node, and determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; Based on the unified feature vector, each distributed controllable and adjustable resource is clustered according to the scheduling period to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; Determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; among which, the active power adjustment amount is used to adjust the load curve of the large power grid; Determine the target curve for peak shaving and valley filling of the large power grid load curve. Establish an objective function with the goal of making the large power grid load curve after adjustment through the active power adjustment of each node as close as possible to the target curve, and determine the constraints of the objective function. The objective function is solved based on the constraints to obtain the adjustment scheme.

2. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 1, characterized in that, Physical parameters characterizing resource regulation capacity include maximum upregulation capacity, maximum downregulation capacity, ramp rate, response time, minimum energy state, and maximum energy state. The parameters characterizing the system load state include the load forecast value, the maximum load forecast value, and the minimum load forecast value; The unified feature vector also includes the magnitude of the load forecast value relative to the minimum load forecast value, and the magnitude of the load forecast value relative to the maximum load forecast value.

3. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 1, characterized in that, An objective function is established with the goal of making the load curve of the large power grid after adjustment through the active power adjustment at each node as close as possible to the target curve. The objective function is as follows: in, For the first Load forecast values ​​for the scheduling period For the first Load target value for the scheduling period For the first Scheduling time period nodes The active power adjustment is T, where T is the total number of scheduling periods and N is the total number of nodes.

4. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 3, characterized in that, Determine the constraints of the objective function, including load node adjustment constraints and network branch constraints.

5. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 4, characterized in that, The load node adjustment constraint is: 。 6. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 4, characterized in that, Network branch constraints are: in, For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding unit injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Nodes in the inverse matrix of the susceptance matrix of the scheduling period node Corresponding load node injection vector Part of For the first Scheduling Node A vector composed of active power predictions. For the first Scheduling Node Active power adjustment The vector formed; branch road susceptance; For the first Active power injection vector of nodes during the scheduling period; branch road The active power limit.

7. The two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid according to claim 1, characterized in that, Based on a unified feature vector, distributed controllable and adjustable resources are clustered according to scheduling time periods to obtain the categories of distributed controllable and adjustable resources in each scheduling time period, including: For a given scheduling period, determine the unified feature vector corresponding to each distributed controllable and adjustable resource under the current scheduling period; construct a sample set based on the unified feature vector of each distributed controllable and adjustable resource; The Euclidean distance between samples is calculated based on the sample set, and the samples are clustered based on an adaptive clustering algorithm to obtain the categories of distributed controllable and adjustable resources in the current scheduling period. The adaptive clustering algorithm automatically determines the final number of clusters and each category by iteratively adjusting the distance threshold and judging whether the change in the number of clusters converges.

8. A two-stage aggregation system for heterogeneous distributed controllable and adjustable resources in a large power grid, characterized in that, The large power grid includes various nodes; the equipment includes: The first processing module is used to determine each distributed controllable and adjustable resource belonging to the same node, and to determine the unified feature vector of the distributed controllable and adjustable resources; wherein, the unified feature vector includes physical parameters characterizing the resource adjustment capability and parameters characterizing the system load status; The second processing module is used to cluster each distributed controllable and adjustable resource according to the scheduling period based on the unified feature vector, so as to obtain the category of distributed controllable and adjustable resources in each scheduling period; wherein, during clustering, the number of clusters is determined based on the sample distance; The third processing module is used to determine the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity of various distributed controllable and adjustable resources in each scheduling period; and to determine the active power adjustment amount of the node in each period based on the adjustment response time, aggregated upward adjustment capacity, and aggregated downward adjustment capacity; wherein, the active power adjustment amount is used to adjust the load curve of the large power grid. The fourth processing module is used to determine the target curve for peak shaving and valley filling of the power grid load curve. The objective function is established with the goal of making the power grid load curve after adjustment by the active power adjustment of each node as close as possible to the target curve, and the constraints of the objective function are determined. The fifth processing module is used to solve the objective function based on the constraints and obtain the adjustment scheme.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the two-stage aggregation method for heterogeneous distributed controllable and adjustable resources in a large power grid as described in any one of claims 1 to 7.

Images