Method, apparatus, device, and medium for determining the executable area of ​​a distributed resource cluster

Abstract

Conventional methods for determining the feasible area of ​​distributed resources in microgrids have the problem of not being able to adequately consider multi-period power flow constraints and operational constraints, making it difficult to accurately calculate the area and not being able to properly reflect output and cost characteristics in the distribution network. [Solution] The present invention relates to a method, apparatus, equipment, and medium for determining the feasible area of ​​a distributed resource cluster. It solves the above problem by constructing a constraint model that includes multi-period coupling power flow constraints of a microgrid, operational constraints and cost constraints of distributed resources, and determining the feasible area of ​​the distributed resources using a convex hull vertex search algorithm for vertex tracking based on this model. The present invention enables calculations that take into account multi-period coupling characteristics, and allows the output and cost characteristics of distributed resources to be accurately reflected in the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of static safety of microgrids, and specifically to a method, apparatus, device, and medium for determining the executable region of a distributed resource cluster.

Background Art

[0002] In the conventional calculation method of the flexible executable region of distributed resources, only the power-cost relationship in a single period is considered, and the period coupling characteristic of power in the distributed resource cluster is ignored. Therefore, the flexible executable region is a two-dimensional convex hull in each period. For resources including period coupling constraints such as stored energy, the power coupling constraints of each period must be considered. In this case, the executable region of the distributed resource cluster is a high-dimensional convex hull. Subsequently, if the current distributed resource aggregation method is used, the calculation of the executable region of the distributed resource cannot be realized, and there are the following problems in the current distributed resource aggregation.

[0003] (1) The flexible executable region of distributed resources including period coupling is a high-dimensional convex hull. In the PVE algorithm (abbreviated as PVE, Progressive vertex enumeration, based on regular vertex search), the computational complexity of obtaining the convex hull from a high-dimensional vertex set is extremely large, resulting in the inability to complete the calculation, and further causing the inability to aggregate the distributed resource cluster.

[0004] (2) The PVE algorithm obtains new vertices by searching each hyperplane of the convex hull. As the number of repetitions increases, the number of hyperplanes increases exponentially, and the computational complexity also rises rapidly.

[0005] (3) Since there are a large number of planes approximating the normal vector to the hyperplane, when the vertex accuracy is constant (for example, 4 significant digits are retained), each repetition includes a large number of duplicate vertices, resulting in a large amount of ineffective calculation.

[0006] Therefore, there is a strong need for a method to determine the feasible area of ​​distributed resources that takes into account multi-period coupling characteristics. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] In view of this, the present invention provides a method, apparatus, device, and medium for determining the executable area of ​​a distributed resource cluster, so as to solve the problem that the executable area of ​​a distributed resource cannot be calculated because multi-period coupling characteristics are not taken into consideration. [Means for solving the problem]

[0008] In a first embodiment, the present invention relates to a method for determining the executable area of ​​a distributed resource cluster, The steps include constructing a microgrid constraint model that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, The present invention provides a method that includes the step of determining the feasible region of a distributed resource using a vertex-tracing convex hull vertex search algorithm based on a microgrid constraint model.

[0009] The method for determining the feasible area of ​​a distributed resource cluster according to the present invention comprehensively considers a microgrid constraint model that includes multi-period coupled power flow constraints of the microgrid, multi-period coupled operation constraints of distributed resources, and cost constraints of distributed resources. Based on the microgrid constraint model, the feasible area of ​​the distributed resources is determined using a convex hull vertex search algorithm for vertex tracking. Distributed resource clusters are aggregated based on the feasible area of ​​the distributed resources. By considering multi-period coupled characteristics, the calculation of the feasible area of ​​the distributed resources is simplified. Ultimately, the aggregated distributed resources reflect the output power and cost characteristics of the distribution network, solving the problem of not being able to calculate the feasible area of ​​distributed resources when multi-period coupled characteristics are not considered.

[0010] In an optional embodiment, the step of constructing a microgrid constraint model that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources is: The steps include acquiring multi-period operating data for a microgrid and constructing multi-period coupled power constraints for the microgrid based on the multi-period operating data for the microgrid, The steps include: obtaining multi-period operational data for distributed resources, and constructing multi-period coupled operational constraints for distributed resources based on the multi-period operational data for distributed resources; The steps include obtaining the power generation cost of distributed resources, determining a cost piecewise linear function based on the power generation cost of distributed resources, and constructing cost constraints for distributed resources based on the cost piecewise linear function. The method includes the step of determining a microgrid constraint model based on multi-period coupled flow constraints of the microgrid, multi-period coupled operation constraints of distributed resources, and cost constraints of distributed resources.

[0011] The method for determining the operational area of ​​a distributed resource cluster according to the present invention constructs multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, respectively. Based on these constraints, it determines a microgrid constraint model and, when calculating the operational area of ​​the distributed resources, considers the multi-period coupled characteristics, provides a constraint model basis for calculating the operational area of ​​the distributed resources, and indirectly improves the accuracy and precision of the calculation of the operational area of ​​the distributed resources.

[0012] In an optional embodiment, the step of determining the feasible region of a distributed resource using a vertex-tracing convex hull vertex search algorithm based on a microgrid constraint model is: Based on a microgrid constraint model, the vertex search is performed using a convex hull vertex search algorithm for vertex tracking to obtain a set of vertices and the corresponding vertex index vectors of the convex hull plane. The steps include obtaining a convex hull plane vertex index vector and a set of clustered direction vectors based on a pre-configured clustering algorithm, The steps include: performing a vertex search based on a clustered set of direction vectors and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane; The method includes the step of determining the executable region of a distributed resource based on a new set of vertices and a new vertex index vector of the corresponding convex hull plane.

[0013] The method for determining the feasible region of a distributed resource cluster according to the present invention uses a vertex-tracing convex hull vertex search algorithm to obtain each surface direction vector of the convex hull without requiring the calculation of the convex hull of the hyperplanar form, extends the vertex-tracing convex hull vertex search algorithm to higher dimensions, further extends microgrid aggregation over multiple periods, performs vertex search based on the clustered direction vector set and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane, determines the feasible region of the distributed resource based on the new set of vertices and the new vertex index vector of the corresponding convex hull plane, and realizes the objective of calculating the feasible region of the distributed resource over multiple periods.

[0014] In an optional embodiment, the step of performing a vertex search using a vertex-tracing convex hull vertex search algorithm based on a microgrid constraint model to obtain a set of vertices and the corresponding vertex index vectors of the convex hull plane is: Based on a microgrid constraint model, the process involves generating multiple direction vectors randomly using a convex hull vertex search algorithm for vertex tracking, and The steps include: performing a vertex search for multiple direction vectors to obtain a set of vertices corresponding to the multiple direction vectors; The method includes the steps of: assigning numbers to the vertices of a set of vertices; selecting and combining multiple non-overlapping vertices; and obtaining a vertex index vector of a convex hull plane of multiple groups containing multiple non-overlapping vertices.

[0015] The method for determining the executable area of ​​a distributed resource cluster according to the present invention is based on an chromatic grid constraint model. It randomly generates multiple direction vectors using a vertex-tracing convex hull vertex search algorithm, performs a vertex search on the multiple direction vectors to obtain a set of vertices corresponding to the multiple direction vectors, assigns numbers to the vertices in the vertex set, selects and combines multiple non-overlapping vertices, and obtains a vertex index vector of a convex hull plane containing multiple non-overlapping vertices. This achieves the objective of obtaining a vertex index vector of a convex hull plane containing multiple non-overlapping vertices. Subsequently, it provides an index basis for obtaining clustered sets of direction vectors.

[0016] In an optional embodiment, the step of obtaining a set of direction vectors clustered based on a convex hull plane vertex index vector and a preset clustering algorithm is: The steps include determining outward normal direction vectors of multiple convex hull surfaces based on vertex index vectors of multiple groups of convex hull planes, The method includes the steps of: performing clustering on outward normal direction vectors of multiple convex hull surfaces using a pre-configured clustering algorithm to obtain a group of clustered direction vectors.

[0017] In an optional embodiment, the step of clustering outward normal direction vectors of multiple convex hull surfaces using a pre-configured clustering algorithm to obtain a clustered set of direction vectors is: The steps include obtaining the density threshold and neighborhood radius of the outward-direction normal vectors of multiple convex hull surfaces, The method includes the steps of: performing clustering on a density threshold and neighborhood radius using a pre-configured clustering algorithm to obtain a group of clustered direction vectors.

[0018] The method for determining the executable region of a distributed resource cluster according to the present invention is to preset a clustering algorithm to cluster data points satisfying high density into one cluster, cluster direction vectors with relatively concentrated directions into one cluster, and finally obtain a clustered group of direction vectors. The obtained group of direction vectors can intensively reflect the direction vectors corresponding to the vertices of the convex hull plane, and provide a vector basis for obtaining a new vertex set subsequently.

[0019] In a selectable embodiment, the step of performing vertex search based on the clustered group of direction vectors and the preset optimization conditions to obtain a new vertex set and a new vertex index vector of the corresponding convex hull plane is as follows: Performing vertex search in each direction of the convex hull surface for the clustered group of direction vectors according to the preset optimization conditions. When a new vertex is searched in a certain direction of the convex hull surface, adding the new vertex to the vertex set to obtain a new vertex set; Numbering the new vertex and adding it to the vertex index vector of the corresponding convex hull plane to obtain a new vertex index vector of the convex hull plane, and including the above steps.

[0020] The method for determining the executable region of a distributed resource cluster according to the present invention is to perform vertex search in each direction of the convex hull surface for the clustered group of direction vectors according to the preset optimization conditions. When a new vertex is searched in a certain direction of the convex hull surface, adding the new vertex to the vertex set to obtain a new vertex set, numbering the new vertex and adding it to the vertex index vector of the corresponding convex hull plane to obtain a new vertex index vector of the convex hull plane, ensuring the purpose of performing vertex search along all surfaces of the convex hull, improving the accuracy and comprehensiveness of vertex search, and providing a vertex basis for calculating the executable region of distributed resources.

[0021] In a second aspect, the present invention is a device for determining the executable region of a distributed resource cluster, wherein A construction module for constructing a microgrid constraint model including multi-period combined power flow constraints of a microgrid, multi-period combined operation constraints of distributed resources, and cost constraints of distributed resources, A determination module for determining an executable region of distributed resources by using a convex hull vertex search algorithm for vertex tracking based on the microgrid constraint model.

[0022] In a third aspect, the present invention provides a computer device including a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute a method for determining an executable region of a distributed resource cluster according to any one of the first aspect or corresponding embodiments thereof.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute a method for determining an executable region of a distributed resource cluster according to any one of the first aspect or corresponding embodiments thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Hereinafter, in order to more clearly explain the specific embodiments of the present invention or the technical solutions in the prior art, the drawings necessary for explaining the specific embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative efforts. [Figure 1] It is a diagram showing the flow of a method for determining an executable region of a distributed resource cluster according to an embodiment of the present invention. [Figure 2] It is a diagram showing the flow of another method for determining an executable region of a distributed resource cluster according to an embodiment of the present invention. [Figure 3]This figure shows a flowchart of a method for determining the executable area of ​​yet another distributed resource cluster according to an embodiment of the present invention. [Figure 4] This figure shows the flow of a convex hull vertex search method based on vertex tracking according to an embodiment of the present invention. [Figure 5] This figure shows the topology of a 9-node microgrid based on an embodiment of the present invention. [Figure 6] This is a logarithmic graph of the number of vertices in the i-th iteration according to an embodiment of the present invention. [Figure 7] This figure shows the gateway power curve and the power deviation curves for each period of a microgrid according to an embodiment of the present invention. [Figure 8] This is a comparison chart showing the proportion of the number of duplicate vertices according to the embodiment of the present invention to the total number of vertices obtained by this iterative search. [Figure 9] This is a comparative diagram of the change over time in the total cost deviation of grid-microgrids according to an embodiment of the present invention. [Figure 10] This is a block diagram of the structure of a device for determining the executable area of ​​a distributed resource cluster according to an embodiment of the present invention. [Figure 11] This figure shows the hardware structure of a computer device according to an embodiment of the present invention. [Modes for carrying out the invention]

[0025] In order to further clarify the object, technical solution, and advantages of the embodiments of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments. However, it is clear that the embodiments described are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments that a person skilled in the art could conceive without creative effort based on the embodiments of the present invention are all within the scope of the present invention.

[0026] According to embodiments of the present invention, a method for determining the executable area of ​​a distributed resource cluster is provided, wherein the steps shown in the flowchart of the drawings can be performed, for example, in a computer system of a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described can be performed in an order different from that specified herein.

[0027] In this embodiment, a method for determining the executable area of ​​a distributed resource cluster capable of running a microgrid is provided. Figure 1 is a flowchart of the method for determining the executable area of ​​a distributed resource cluster according to an embodiment of the present invention. As shown in Figure 1, the flow includes the following steps S101 and S102.

[0028] In step S101, a microgrid constraint model is constructed that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources.

[0029] Specifically, a microgrid, also known as a minigrid, refers to a small-scale power generation and distribution system composed of distributed resources, energy storage devices, energy converters, loads, monitoring and protection devices, etc. Each node is equivalent to a distributed resource, energy storage device, energy converter, load, monitoring and protection device, and a microgrid is equivalent to a small-scale power generation and distribution system composed of multiple nodes, which can operate independently and be interconnected with higher-level grids.

[0030] The process is extended from a single period to a period of T, where T is set according to the actual situation, and T can take values ​​from 1:00 to 24:00. This allows for a conversion from a two-dimensional convex hull to a higher-dimensional convex hull when subsequently calculating the usable area of ​​distributed resources, improving the accuracy of the calculation of the usable area of ​​distributed resources.

[0031] Multi-period coupled power flow constraints in a microgrid are power flow calculations performed in a microgrid distribution system when certain quantities are optimized or subject to other constraints. These constraints include, but are not limited to, the amplitude range of node voltages, limits on the active and reactive power of power nodes, and constraints on the voltage phase difference between nodes. Multi-period coupled power flow constraints in a microgrid ensure the operational stability of the microgrid distribution system.

[0032] The multi-period coupled operation constraints of distributed resources primarily reflect parameter constraints such as power, network capacity, and charge state during the power generation operation of distributed resources, as well as the distributed power output of each node in a microgrid.

[0033] The cost constraints of distributed resources include the costs associated with operating each distributed power generation resource, and reflect the constraints imposed by factors such as the time and transaction costs incurred by the distributed resources during power generation.

[0034] In step S102, the feasible region of the distributed resource is determined using a vertex-tracing convex hull vertex search algorithm based on the microgrid constraint model.

[0035] Specifically, the vertex-tracing convex hull vertex search algorithm is an improvement on the PVE (Progressive vertex enumeration, abbreviated as PVE) algorithm. The vertex-tracing convex hull vertex search algorithm, given an N-dimensional convex hull V, can perform vertex searches and further extend the hull by knowing the outward normal vectors of all surfaces of the hull. The core of calculating the outward normal vectors of the hull surfaces is obtaining the vertex index vectors for each surface of the hull. The vertex index vectors are the indices of points on a given hull surface, corresponding one-to-one with each surface. This improves the dimensionality of the hull and reduces the enormous computational complexity of the hull calculation.

[0036] Based on a microgrid constraint model, the vertices of the feasible region of a distributed resource are determined using a vertex-tracing convex hull vertex search algorithm. The boundaries of the feasible region of the distributed resource are then constructed using these vertices, and finally, the feasible region of the distributed resource is computed.

[0037] The method for determining the feasible area of ​​a distributed resource cluster according to the present invention comprehensively considers a microgrid constraint model that includes multi-period coupled power flow constraints of the microgrid, multi-period coupled operation constraints of distributed resources, and cost constraints of distributed resources. Based on the microgrid constraint model, the feasible area of ​​the distributed resources is determined using a convex hull vertex search algorithm for vertex tracking. Distributed resource clusters are aggregated based on the feasible area of ​​the distributed resources. By considering multi-period coupled characteristics, the calculation of the feasible area of ​​the distributed resources is simplified. The aggregated distributed resources reflect the output power and cost characteristics of the distribution network, solving the problem where the feasible area of ​​the distributed resources cannot be calculated if multi-period coupled characteristics are not considered.

[0038] In this embodiment, a method for determining the executable area of ​​a distributed resource cluster capable of running a microgrid is provided. Figure 2 is a flowchart of the method for determining the executable area of ​​a distributed resource cluster according to an embodiment of the present invention. As shown in Figure 2, the flow includes the following steps S201 and S202.

[0039] In step S201, a microgrid constraint model is constructed that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources.

[0040] Specifically, step S201 above includes the following steps S2011 to S2014.

[0041] In step S2011, multi-period operation data of the microgrid is acquired, and multi-period coupled power constraints for the microgrid are constructed based on the multi-period operation data of the microgrid.

[0042] Specifically, the basic data for multi-period operation of a microgrid includes the number of nodes in the microgrid, the node voltage corresponding to the multi-period, and the node phase angle corresponding to the multi-period.

[0043] Specifically, the tidal flow equation constraints for the microgrid are expressed by the following equation. JPEG2026059715000002.jpg85170

[0044] In step S2012, multi-period operation data for distributed resources is acquired, and multi-period coupled operation constraints for distributed resources are constructed based on the multi-period operation data for distributed resources.

[0045] For example, the multi-period coupled operation constraints of distributed resources are represented by the operation constraints of distributed stored energy resources, with the multi-period being 1:00 to 24:00. In this example, the multi-period operation data of the distributed resources includes all stored energy active power, stored energy charge state, upper and lower limits of stored energy electrostatic discharge active power, and stored energy battery active power within 1:00 to 24:00.

[0046] The constraints on multi-period coupled operation of distributed resources are expressed by the following equation. JPEG2026059715000003.jpg175170

[0047] In step S2013, the power generation cost of the distributed resources is obtained, a cost piecewise linear function is determined based on the power generation cost of the distributed resources, and cost constraints for the distributed resources are constructed based on the cost piecewise linear function.

[0048] JPEG2026059715000004.jpg41170

[0049] Piecewise linear functions are expressed as follows: JPEG2026059715000005.jpg78170

[0050] We construct a cost constraint for distributed resources based on a cost segmentation linear function, and express the cost constraint for distributed resources by the following equation. JPEG2026059715000006.jpg34170

[0051] In step S2014, the microgrid constraint model is determined based on the multi-period coupled flow constraint of the microgrid, the multi-period coupled operation constraint of the distributed resources, and the cost constraint of the distributed resources.

[0052] Specifically, the multi-period coupled flow constraints of the microgrid, the multi-period coupled operation constraints of the distributed resources, and the cost constraints of the distributed resources are placed in a three-dimensional coordinate system to obtain a polyhedron with a convex hull, and the model represented by this polyhedron with a convex hull is defined as the microgrid constraint model.

[0053] In step S202, the feasible region of the distributed resources is determined using a vertex-tracing convex hull vertex search algorithm based on the microgrid constraint model. For details, please refer to step S102 of the embodiment shown in Figure 1, and redundant explanations will be omitted here.

[0054] The method for determining the usable area of ​​a distributed resource cluster according to this embodiment constructs multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, respectively. Based on these constraints, a microgrid constraint model is determined, and when calculating the usable area of ​​the distributed resources, multi-period coupled characteristics are taken into consideration, providing a constraint model basis for calculating the usable area of ​​the distributed resources, thereby indirectly improving the accuracy and precision of the calculation of the usable area of ​​the distributed resources.

[0055] In this embodiment, a method for determining the executable area of ​​a distributed resource cluster capable of running a microgrid is provided. Figure 3 is a flowchart of the method for determining the executable area of ​​a distributed resource cluster according to an embodiment of the present invention. As shown in Figure 3, the flow includes the following steps S301 and S302.

[0056] In step S301, a microgrid constraint model is constructed that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources. For details, please refer to step S201 of the embodiment shown in Figure 2, and redundant explanations will be omitted here.

[0057] In step S302, the feasible region of the distributed resource is determined using a vertex-tracing convex hull vertex search algorithm based on the microgrid constraint model.

[0058] Specifically, as shown in Figure 4, step S302 includes steps S3021 to S3024.

[0059] In step S3021, based on the microgrid constraint model, a vertex search is performed using a convex hull vertex search algorithm for vertex tracking to obtain the vertex set and the corresponding vertex index vector of the convex hull plane.

[0060] In an optional embodiment, step S3021 includes the following steps a1 to a3.

[0061] In step a1, based on the microgrid constraint model, multiple direction vectors are randomly generated using a convex hull vertex search algorithm for vertex tracking.

[0062] Specifically, assuming that a polyhedron with a convex hull represents a microgrid-constrained model having an N-dimensional convex hull V, if we know the outward normal direction vectors of all surfaces of the N-dimensional convex hull V, we can perform a vertex search and further extend the convex hull. For the N-dimensional problem, we generate N+1 direction vectors using a random generation method.

[0063] In step a2, a vertex search is performed for multiple direction vectors to obtain a set of vertices corresponding to the multiple direction vectors.

[0064] Specifically, a vertex search is performed along the N+1 directions of the N+1 direction vectors to obtain a vertex set V containing N+1 vertices.

[0065] In step a3, the vertices of the vertex set are numbered, multiple non-overlapping vertices are selected and combined to obtain the vertex index vector of the convex hull plane of multiple groups containing multiple non-overlapping vertices.

[0066] JPEG2026059715000007.jpg45170

[0067] In step S3022, a convex hull plane vertex index vector and a group of clustered direction vectors based on a pre-set clustering algorithm are obtained.

[0068] In an optional embodiment, step S3022 includes the following steps b1, b2.

[0069] In step b1, outward normal vectors of multiple convex hull surfaces are determined based on the vertex index vectors of the multiple groups of convex hull planes.

[0070] Specifically, for the i-th vertex index vector, the outward normal direction vector of the i-th convex hull surface can be obtained based on the formula, and all direction vectors of the convex hull can be calculated sequentially.

[0071] JPEG2026059715000008.jpg107170

[0072] In step b2, clustering is performed on the outward normal direction vectors of multiple convex hull surfaces using a pre-configured clustering algorithm to obtain a clustered set of direction vectors.

[0073] In an optional embodiment, step b2 is achieved using the following flow.

[0074] The density threshold and neighborhood radius of the outward-pointing normal direction vectors of multiple convex hull surfaces are obtained, and clustering is performed on the density threshold and neighborhood radius using a pre-configured clustering algorithm to obtain a clustered set of direction vectors.

[0075] Specifically, the pre-configured clustering algorithm is a density-based clustering algorithm, namely the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise, abbreviated as DBSCAN). The main idea of ​​the DBSCAN algorithm is to cluster data points that satisfy high density into a single cluster, and it can cluster directional vectors with relatively concentrated orientations into a single cluster.

[0076] The DBSCAN algorithm has two input parameters: the density threshold MinPts for outward normal direction vectors and the neighboring region radius Eps. To preserve the characteristics of the direction vectors to the greatest extent possible, MinPts = 1 and the neighboring region radius Eps is set to one-tenth of the maximum distance of the direction vectors. JPEG2026059715000009.jpg39170 A new set of direction vectors is constructed. Clustering can reduce the number of direction vectors and the number of vertex searches, further reducing the computational complexity and improving computational efficiency.

[0077] In step S3023, a vertex search is performed based on the clustered direction vectors and the pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector for the corresponding convex hull plane.

[0078] In an optional embodiment, step S3023 includes the following steps c1, c2.

[0079] In step c1, a vertex search is performed in each direction of the convex hull surface on the clustered direction vectors according to a predetermined optimization condition. When a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set, and a new vertex set is obtained.

[0080] Specifically, using the constraint of multi-period operation of distributed resources as an example, and aiming for the lowest possible cost for the microgrid, we substitute the direction vector into the optimization condition and perform a vertex search. When a new vertex is obtained by searching along the i-th direction vector, the new vertex is added to the vertex set V, and a new vertex set V' is obtained.

[0081] In step c2, the new vertices are numbered and added to the corresponding vertex index vector of the convex hull plane, thereby obtaining a new vertex index vector of the convex hull plane.

[0082] Specifically, the newly added vertices are numbered and added to the i-th vertex index vector, which in this case contains N+1 vertices. If no new vertices are obtained, no operation is performed.

[0083] For all vertex index vectors containing N+1 vertices, a rearrangement is performed to obtain a new vertex index vector for the convex hull plane. If the number of points in the vertex set exceeds a certain number M (where M is set according to the actual situation), a new vertex set V' is output.

[0084] In step S3024, the executable region of the distributed resource is determined based on the new set of vertices and the new vertex index vector of the corresponding convex hull plane.

[0085] Specifically, in the kth iteration, k along the convex hull surface p If n new vertices have been found, then based on the new vertices and the vertex index vector to which they are located, at most k pN new vertex index vectors (without considering duplicate vertex indices), i.e., k p N convex hull surfaces can be generated. p The plane enclosed by N convex hull surfaces represents the executable region of distributed resources.

[0086] In a vertex-tracing convex hull vertex search algorithm, the number of new convex hull surfaces generated in each iteration increases linearly with the number of iterations, while in the PVE algorithm, the number of new convex hull surfaces increases exponentially with the number of iterations. Based on the new vertex set and the new vertex index vector of the corresponding convex hull plane, it is possible to compute a high-dimensional distributed executable region.

[0087] The method for determining the feasible region of a distributed resource cluster according to this embodiment uses a vertex-tracing convex hull vertex search algorithm to obtain each surface direction vector of the convex hull without requiring the calculation of the convex hull of the hyperplanar form. This method extends the vertex-tracing convex hull vertex search algorithm to higher dimensions and further extends microgrid aggregation over multiple periods. Vertex search is performed based on the clustered direction vectors and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane. The feasible region of the distributed resource is determined based on the new set of vertices and the new vertex index vector of the corresponding convex hull plane, thereby achieving the objective of calculating the feasible region of the distributed resource over multiple periods. Furthermore, distributed resource clusters can be aggregated based on the executable domain of the distributed resources. Specifically, the high-dimensional vertex convex hull of the executable domain of the distributed resources is used as the aggregation model for distributed resource clusters, thereby aggregating distributed resource clusters in an actual microgrid. The aggregation model for distributed resource clusters achieves information encapsulation and resolution of internal variables for the microgrid power distribution system and distributed resources, and can reflect the output power and cost characteristics of the microgrid power distribution system.

[0088] After calculating the usable area of ​​distributed resources using the method for determining the usable area of ​​a distributed resource cluster according to this embodiment, the obtained usable area is used as a microgrid aggregation model to participate in the grid's optimization scheduling, specifically as follows.

[0089] As shown in Figure 5, using a 9-node microgrid topology as an example, the method for determining the usable area of ​​a distributed resource cluster according to this embodiment is adopted to calculate the usable area of ​​the distributed resource cluster in the 9-node microgrid, obtaining the usable area for each iteration, and using the usable area obtained in each iteration as an aggregation model for the microgrid to participate in the grid's optimization scheduling.

[0090] Specifically, the microgrid employs an IEEE 9-node distribution network and calculates an aggregated model for 11:00-15:00. The IEEE 9-node distribution network includes two adjustable buildings connected to nodes 2 and 4, two distributed solar power plants connected to nodes 1 and 3, and three distributed energy storage plants connected to nodes 6, 8, and 9, respectively. Boundary operating conditions include power load and solar output. Hourly data is assumed to be based on foundational data collected from actual distribution networks over 24 hours, including load and irradiance data. The grid system to which the microgrid connects consists of centralized thermal power plants, and grid-side network constraints are not considered.

[0091] The executable area information obtained in each iteration is shown in Table 1 below. Table 1 JPEG2026059715000010.jpg54170

[0092] As shown in Figure 6, this is a logarithmic graph of the number of vertices in the i-th iteration.

[0093] The high-dimensional feasible region obtained in each iterative calculation is used as a microgrid aggregation model and involved in grid operation scheduling. As shown in Figure 7, the gateway power curve and power deviation for each period are obtained, and the change over time of the total grid-microgrid cost deviation for each iteration is shown in Table 2 below. JPEG2026059715000011.jpg29170

[0094] Figure 8 shows the ratio of the number of duplicate vertices found in each iteration to the total number of vertices found in the current iteration, calculated using the PVE algorithm (Progressive vertex enumeration, abbreviated as PVE) and the vertex-tracking convex-hull vertex search algorithm. From the second iteration onward, the ratio of duplicate vertices found using the vertex-tracking convex-hull vertex search algorithm is lower than that of the PVE algorithm, indicating that the DBSCAN cluster direction vector effectively reduces the amount of wasted computation. Figure 9 shows a curve illustrating the decrease in the total cost deviation of the grid-microgrid obtained using the PVE algorithm and the vertex-tracking convex-hull vertex search algorithm over time. Since the time axis is large, the horizontal axis is logarithmic and the initialization time is set to 0.01 s. As can be seen from Figure 9, the total cost error decreases rapidly when the vertex-tracking convex-hull vertex search algorithm is used.

[0095] The method for determining the feasible region of a distributed resource cluster according to this embodiment uses a vertex-tracing convex hull vertex search algorithm to obtain each surface direction vector of the convex hull without requiring the calculation of the convex hull of the hyperplanar form. This extends the vertex-tracing convex hull vertex search algorithm to higher dimensions and further extends microgrid aggregation over multiple periods. Vertex search is performed based on the clustered direction vectors and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane. The feasible region of the distributed resource is determined based on the new set of vertices and the new vertex index vector of the corresponding convex hull plane, thereby achieving the objective of calculating the feasible region of the distributed resource over multiple periods. The calculated feasible region of the distributed resource over multiple periods achieves the objective of exploring high-dimensional convex hull vertices and constructing a distributed resource cluster aggregation model that includes stored energy. A 9-node microgrid case is selected to achieve the objective of demonstrating the feasibility of the multi-period distributed resource aggregation model in which grid cooperative optimization is involved.

[0096] In this embodiment, a device for determining the executable area of ​​a distributed resource cluster is further provided, which is used to implement the above embodiment and preferred embodiments, and redundant explanations of what has already been described are omitted. The term "module" used below refers to a combination of software and / or hardware capable of implementing a predetermined function. The devices described in the following embodiments are preferably implemented in software, but can also be implemented in hardware, or a combination of software and hardware, and are conceived accordingly.

[0097] This embodiment provides a device for determining the executable area of ​​a distributed resource cluster, and as shown in Figure 10, the device is A construction module 1001 for constructing a microgrid constraint model that includes multi-period coupled flow constraints for microgrids, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, The system includes a decision module 1002 for determining the feasible region of a distributed resource using a vertex-tracing convex hull vertex search algorithm based on a microgrid constraint model.

[0098] In some select embodiments, the construction module 1001 is A first construction unit for acquiring multi-period operating data of a microgrid and constructing multi-period coupled power constraints for the microgrid based on the multi-period operating data of the microgrid, A second construction unit for acquiring multi-period operational data of distributed resources and constructing multi-period coupled operational constraints for distributed resources based on that multi-period operational data, A third construction unit for obtaining the power generation cost of distributed resources, determining a cost segment linear function based on the power generation cost of distributed resources, and constructing cost constraints for distributed resources based on the cost segment linear function, It includes a first decision unit for determining a microgrid constraint model based on multi-period coupled flow constraints of the microgrid, multi-period coupled operation constraints of distributed resources, and cost constraints of distributed resources.

[0099] In some select embodiments, the decision module 1002 is Based on a microgrid constraint model, a first search unit performs vertex search using a convex hull vertex search algorithm for vertex tracking to obtain a set of vertices and the corresponding vertex index vectors of the convex hull plane. A clustering unit for obtaining a set of direction vectors clustered based on a convex hull plane vertex index vector and a pre-configured clustering algorithm, A second search unit performs vertex search based on a clustered set of direction vectors and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane. It includes a second decision unit for determining the feasible region of a distributed resource based on a new set of vertices and a new vertex index vector of the corresponding convex hull plane.

[0100] In some selectable embodiments, the first search unit is: Based on a microgrid constraint model, a generator subunit for randomly generating multiple direction vectors using a vertex-tracing convex hull vertex search algorithm, A first search subunit for performing vertex searches for multiple direction vectors and obtaining a set of vertices corresponding to multiple direction vectors, This includes a combination subunit for assigning numbers to the vertices of a vertex set, selecting and combining multiple non-overlapping vertices, and obtaining a vertex index vector of a convex hull plane of multiple groups containing multiple non-overlapping vertices.

[0101] In some optional embodiments, the clustering unit is A first determination subunit for determining outward normal direction vectors of multiple convex hull surfaces based on vertex index vectors of multiple groups of convex hull planes, It includes a clustering subunit for performing clustering on outward normal direction vectors of multiple convex hull surfaces using a pre-configured clustering algorithm to obtain a clustered set of direction vectors.

[0102] In some selectable embodiments, the second search unit is: A second search subunit is used to obtain a new vertex set by performing a vertex search in each direction of the convex hull surface on a cluster of direction vectors clustered according to pre-set optimization conditions, and when a new vertex is searched in a certain direction of the convex hull surface, the new vertex is added to the vertex set, and a new vertex set is obtained. This includes an additional subunit for numbering new vertices and adding them to the corresponding vertex index vector of the convex hull plane, thereby obtaining a new vertex index vector of the convex hull plane.

[0103] Further functional descriptions of each of the above modules and units are the same as those in the corresponding embodiments described above, and redundant explanations are omitted here. In this embodiment, the device for determining the executable area of ​​a distributed resource cluster is represented in the form of a functional unit, where the unit is an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices capable of providing the above functions.

[0104] An embodiment of the present invention further provides a computer device having a device for determining the executable area of ​​a distributed resource cluster as shown in Figure 10.

[0105] Referring to Figure 11, which is a diagram showing the structure of a computer device according to a selectable embodiment of the present invention, as shown in Figure 11, the computer device includes one or more processors 10, memory 20, and interfaces for connecting each component, including a high-speed interface and a low-speed interface. Each component is connected to communicate with each other using different buses and is mounted on a common motherboard or in other forms as needed. The processors can process instructions, including instructions stored in or on memory, to cause GUI graphics information to be displayed on an external input / output device (e.g., a display device coupled to the interface) which are executed within the computer device. In some selectable embodiments, multiple processors and / or multiple buses can be used, along with multiple memories, as needed. Similarly, multiple computer devices can be connected, each providing a portion of the necessary operations (e.g., a server array, a group of blade servers, or a multiprocessor system). In Figure 11, one processor 10 is used as an example.

[0106] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Here, the processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field-programmable logic gate array, a universal array logic, or any combination thereof.

[0107] Here, the memory 20 stores instructions that can be executed by at least one processor 10, so as to cause at least one processor 10 to execute the method shown in the above embodiment.

[0108] Memory 20 may include a program storage area capable of storing an operating system and application programs required for at least one function, and a data storage area capable of storing data created in accordance with the use of the computer equipment. Furthermore, memory 20 may include high-speed random-access memory and may further include non-temporary memory such as at least one magnetic disk storage device, a flash memory device, or other non-temporary solid-state storage device. In some optional embodiments, memory 20 optionally includes memory located remotely from the processor 10, and these remote memories can be connected to the computer equipment via a network. Examples of the network include, but are not limited to, the Internet, a corporate intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] Memory 20 may include volatile memory such as random access memory, or non-volatile memory such as flash memory, hard disk, or solid-state drive, and furthermore, memory 20 may include a combination of the above types of memory.

[0110] The computer equipment further includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected by a bus or other means, with Figure 11 showing a bus connection as an example.

[0111] The input device 30 can receive input numerical or character information and generate key signal inputs related to user settings and function control of the computer equipment, such as a touch panel, keypad, mouse, trackpad, pointing stick, one or more mouse buttons, trackball, joystick, etc. The output device 40 may include a display device, auxiliary lighting device (e.g., LEDs), and haptic feedback device (e.g., vibration motor). The display device includes, but is not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some selectable embodiments, the display device may be a touch panel.

[0112] Embodiments of the present invention further provide computer-readable storage media, and the methods according to the embodiments of the present invention described above can be implemented in hardware, firmware, or so that they can be recorded on a storage medium, or as computer code stored on a local storage medium, which is downloaded over a network, on a remote storage medium or a non-temporary machine-readable storage medium, thereby allowing the methods described herein to be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Here, the storage medium may be a magnetic disk, an optical disk, read-only memory, random access memory, flash memory, a hard disk, or a solid-state drive, and furthermore, the storage medium may include a combination of the above types of memory. The computer, processor, microprocessor controller, or programmable hardware includes a storage assembly capable of storing or receiving software or computer code, and it should be understood that the methods shown in the embodiments are implemented when the software or computer code is accessed and executed by the computer, processor, or hardware.

[0113] Although embodiments of the present invention have been described above in accordance with the drawings, those skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention, and such modifications and alterations are all included within the scope limited by the appended claims.

Claims

1. A method for determining the executable area of ​​a distributed resource cluster, The steps include constructing a microgrid constraint model that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, A method for determining the feasible region of a distributed resource cluster, comprising the step of determining the feasible region of a distributed resource using a convex hull vertex search algorithm for vertex tracking, based on the microgrid constraint model.

2. The step of constructing a microgrid constraint model that includes multi-period coupled flow constraints for the microgrid, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, The steps include acquiring multi-period operating data of a microgrid and constructing multi-period coupled power constraints for the microgrid based on the multi-period operating data of the microgrid, The steps include: acquiring multi-period operation data of distributed resources, and constructing multi-period coupled operation constraints for distributed resources based on the multi-period operation data of the distributed resources; The steps include obtaining the power generation cost of distributed resources, determining a cost segment linear function based on the power generation cost of the distributed resources, and constructing cost constraints for the distributed resources based on the cost segment linear function, The method according to claim 1, comprising the step of determining a microgrid constraint model based on multi-period coupled flow constraints of the microgrid, multi-period coupled operation constraints of distributed resources, and cost constraints of distributed resources.

3. Based on the aforementioned microgrid constraint model, the step of determining the feasible region of a distributed resource using a vertex-tracing convex hull vertex search algorithm is as follows: Based on the microgrid constraint model, the step of performing a vertex search using a convex hull vertex search algorithm for vertex tracking to obtain a set of vertices and the corresponding vertex index vector of the convex hull plane, The steps include obtaining a group of direction vectors clustered based on the aforementioned convex hull plane vertex index vectors and a pre-set clustering algorithm, The steps include: performing a vertex search based on the clustered set of direction vectors and pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane; The method according to claim 1, comprising the step of determining an executable region of a distributed resource based on the new set of vertices and the new vertex index vector of the corresponding convex hull plane.

4. Based on the aforementioned microgrid constraint model, the step of performing a vertex search using a convex hull vertex search algorithm for vertex tracking to obtain a set of vertices and the corresponding vertex index vector of the convex hull plane is as follows: Based on the aforementioned microgrid constraint model, the steps include: randomly generating multiple direction vectors using a convex hull vertex search algorithm for vertex tracking; The steps include: performing a vertex search for multiple direction vectors to obtain a set of vertices corresponding to the multiple direction vectors; The method according to the previous claim, comprising the steps of: assigning numbers to the vertices of the vertex set; selecting and combining multiple non-overlapping vertices to obtain a vertex index vector of a convex hull plane of multiple groups containing multiple non-overlapping vertices.

5. The step of obtaining a set of direction vectors clustered based on the vertex index vectors of the convex hull plane and a pre-set clustering algorithm is: The steps include determining outward normal direction vectors of multiple convex hull surfaces based on vertex index vectors of multiple groups of convex hull planes, The method according to claim 4, characterized by comprising the step of performing clustering on the outward normal direction vectors of the plurality of convex hull surfaces using a pre-set clustering algorithm to obtain a clustered group of direction vectors.

6. The step of clustering the outward normal direction vectors of the multiple convex hull surfaces using a pre-configured clustering algorithm to obtain a group of clustered direction vectors is: The steps include obtaining the density threshold and neighborhood radius of the outward-direction normal vectors of multiple convex hull surfaces, The method according to claim 5, characterized by comprising the step of performing clustering on the density threshold and neighborhood radius using a pre-set clustering algorithm to obtain a clustered group of direction vectors.

7. The step of performing a vertex search based on the clustered set of direction vectors and the pre-set optimization conditions to obtain a new set of vertices and a new vertex index vector of the corresponding convex hull plane is as follows: The process involves performing a vertex search in each direction of the convex hull surface on a cluster of direction vectors clustered according to pre-set optimization conditions, searching for a new vertex in a certain direction of the convex hull surface, adding the new vertex to the vertex set, and obtaining a new vertex set. The method according to claim 5, comprising the step of assigning numbers to the new vertices and adding them to the corresponding vertex index vector of the convex hull plane to obtain a new vertex index vector of the convex hull plane.

8. A device for determining the executable area of ​​a distributed resource cluster, A construction module for building a microgrid constraint model that includes multi-period coupled flow constraints for microgrids, multi-period coupled operation constraints for distributed resources, and cost constraints for distributed resources, A device for determining the feasible region of a distributed resource cluster, characterized by including a decision module for determining the feasible region of a distributed resource using a vertex-tracing convex hull vertex search algorithm based on the microgrid constraint model.

9. Computer equipment, A computer device comprising memory and a processor, wherein the memory and the processor are interconnected in a manner that enables mutual communication, the memory stores computer instructions, and the processor executes the method for determining the executable area of ​​a distributed resource cluster according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium characterized by storing computer instructions for causing a computer to execute the method for determining the executable area of ​​a distributed resource cluster according to any one of claims 1 to 7.

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