CN109522964B - Clustering method and device for virtual power plant, regulation and control equipment and computer storage medium - Google Patents
Clustering method and device for virtual power plant, regulation and control equipment and computer storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a clustering method and device of a virtual power plant, regulation and control equipment and a computer readable storage medium. The method comprises the following steps: determining a leader node in each node of the power system through a preset clustering numerical value, wherein the nodes except the leader node in all the nodes are taken as following nodes; respectively calculating a first distance from each following node to each leading node by adopting a first preset algorithm; selecting a following node, and selecting a leader node corresponding to the shortest distance in first distances from the following node to the leader nodes; and clustering the following nodes to the selected leader node until all the following nodes are clustered. The flexibility, the operation efficiency and the response efficiency of the virtual power plant are improved, so that the dependence on the traditional power generation mode is reduced, and the economic cost and the environmental cost for maintaining the stability of the power system are reduced.
Description
Technical Field
The invention relates to the technical field of virtual power plant resource clustering, in particular to a virtual power plant clustering method, a virtual power plant clustering device, a regulating and controlling device and a computer readable storage medium.
Background
A Virtual Power Plant (Virtual Power Plant) is an energy aggregation form that integrates distributed Power generation equipment, energy storage equipment and controllable loads and participates in the electric Power market and the auxiliary service market as a whole. With the development of the smart grid, higher requirements are put forward on the response speed of the flexible resources and the response reliability of the resources, but the characteristics of different types of flexible resources are different. The development of power sources and loads enables a future power distribution network to have the characteristics of small distributed capacity, various resource types, bidirectional tide, interactivity and the like, the virtual power plant technology is effectively connected with distributed resources and a power system, the resource integration and distribution are realized, and the dual roles of safe and stable operation of a power grid and a power market are considered. The resources are classified and are pertinently served, and the method has important significance for improving the flexibility of the virtual power plant and the response efficiency of the operation efficiency. The different types and the different quantities of the flexible resources integrated by the different virtual power plants affect the aggregation characteristics of the virtual power plants, so that the auxiliary services which can be participated by the virtual power plants are affected.
Through clustering the internal flexible resources of the virtual power plant, the quality of service provided by the virtual power plant for a power grid can be improved from the aspect of system operation, and the increase of the income of the virtual power plant is facilitated from the aspect of individual flexible resources. The size of the power network varies, and a distributed clustering method based on a Multi-agent network (Multi-agent system) is suitable for any level of partition. At present, the research on a flexible resource distributed clustering method considering the topological structure of a power grid is less.
Disclosure of Invention
Embodiments of the present invention provide a clustering method, an apparatus, a regulation and control device, and a computer-readable storage medium for a virtual power plant, so as to improve flexibility, operation efficiency, and response efficiency of the virtual power plant, thereby reducing dependence on a conventional power generation manner, and reducing economic cost and environmental cost for maintaining stability of a power system.
In a first aspect, an embodiment of the present invention provides a clustering method for a virtual power plant, where the method includes:
determining a leader node in each node of the power system through a preset clustering numerical value, wherein the nodes except the leader node in all the nodes are taken as following nodes;
respectively calculating a first distance from each following node to each leading node by adopting a first preset algorithm;
selecting one following node optionally, and selecting a leader node corresponding to the shortest distance from the following node to each leader node;
and clustering the following nodes to the selected leader node until all the following nodes are clustered.
In a second aspect, an embodiment of the present invention further provides a clustering device for a virtual power plant, where the device includes:
the leader node acquisition module is used for determining a leader node in each node of the power system through a preset clustering value, wherein nodes except the leader node in all the nodes are taken as following nodes;
the shortest distance determining module is used for respectively calculating first distances from the following nodes to the leader nodes by adopting a first preset algorithm;
the leader node selection module is used for selecting a following node optionally and selecting a leader node corresponding to the shortest distance in the first distances from the following node to the leader nodes;
and the clustering module is used for clustering the following nodes to the selected leader node until all the following nodes are clustered.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the clustering method for virtual power plants provided by the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a regulation and control device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the clustering method for the virtual power plant provided by the embodiment of the present invention.
In the embodiment of the invention, a leader node is determined in each node of the power system through a preset clustering value, wherein nodes except for the leader node in all the nodes are taken as following nodes, a first preset algorithm is adopted to calculate the first distance from each following node to each leader node, one following node is selected optionally, the leader node corresponding to the shortest distance in the first distances from the following node to each leader node is selected, and the following nodes are clustered to the selected leader node until all the following nodes are clustered. The flexibility, the operation efficiency and the response efficiency of the virtual power plant are improved, so that the dependence on the traditional power generation mode is reduced, and the economic cost and the environmental cost for maintaining the stability of the power system are reduced.
Drawings
FIG. 1 is a schematic flow chart of a clustering method for a virtual power plant according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another clustering method for a virtual power plant according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a leader node determining method according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a clustering device of a virtual power plant according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a regulating device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a schematic flow chart of a clustering method for a virtual power plant according to an embodiment of the present invention, where the method may be executed by a control device according to an embodiment of the present invention, and the control device may be implemented in a software and/or hardware manner, and the method specifically includes the following steps:
the invention is based on a discrete consistency algorithm with offset, and realizes the electrical characteristic clustering of a virtual power plant based on flexible resources in a power system. When virtual power plants which are widely distributed and have large quantity exist in the power network, the method can realize the classification of the flexible resources in a distributed manner in the whole network according to the flexible resource constitution and the integration characteristic, thereby providing a basis for the function distribution of the flexible resources, classifying the flexible resources and pertinently providing auxiliary service for the power system. Each node in the power grid only needs to collect local information and neighbor information, compared with a centralized clustering algorithm, the requirement for information amount is reduced, and meanwhile, the privacy of users is also protected. The invention adopts a distributed method, and only local power information and operation parameters are needed to make a scheduling instruction for realizing global optimization.
Before resource clustering, a leader node is determined in each node of the power system according to a preset clustering value, and the rest nodes are taken as follower nodes. In this embodiment, the method for determining the leader node may be divided into two methods, one is randomly selected, and the other is obtained by calculation through a distributed K-means + + algorithm. The method specifically comprises the following steps: if the preset clustering value is k, one method is to randomly select k nodes from all nodes of the power system as leader nodes, and the other nodes as follower nodes; in another method, a local agent of each node in the power system executes a distributed K-means + + algorithm, and determines K nodes as leader nodes and the other nodes as follower nodes according to a specified preset clustering value K.
in this embodiment, the first preset algorithm is a minimum consistency algorithm of discrete offsets, and the local agent of each node executes the minimum consistency algorithm of discrete offsets to calculate a shortest distance from each following node to each leader node, where the shortest distance is the first distance.
Before calculating the shortest distance from each following node to each leading node, the local agent of each node calculates the electrical characteristic distance between each node and the connected node according to the topological information of the power system and the characteristics of the flexible resources of the power system. The classification of flexible resources requires consideration of the following factors:
response capability: including the ability to adjust the load (up-range c) u And a down-regulation interval c d ) And climbing ability r, etc.;
response characteristics: mainly comprising the speed s of resource response, the duration T of advance notice and the like;
degree of response: the method comprises the steps of responding times, adjustable time period, maintaining duration and the like;
illustratively, since each component in the feature vector of the resource faces the problem of normalization, i.e., components with large variance will take more weight. To solve this problem, a variance calculation is introduced. The individual components are normalized, for example:
after this operation, the original feature vector (c) u ,c d R, s, T) into
Suppose there are two virtual power plants, each being ^ er>
And &>
Define the distance between them as:
the above distance is understood to be the electrical characteristic distance between the connected nodes.
The specific principle of the minimum consistency algorithm of the discrete offset is as follows:
assuming that the number of nodes in the power network topology is N, the point set N may be divided into two point sets: s1 is a point set of the leader node, and S2 is a point set of the follower node. The state quantity of the leader node is kept unchanged and is not influenced by the state quantities of other nodes, and the state quantity of the following node is determined by the state quantity of the leader node. Point set N i Is a set of points, x, of nodes i neighbors i Is the state value of the node.
It can be understood that, after the electrical characteristic distances between the connected nodes in the power system are obtained, when the minimum consistency algorithm of the discrete offset is executed to calculate the shortest distance from each following node to each leader node, since there is no direct connection relationship between a certain following node and the leader node, the following node will necessarily pass through one or more transit connection nodes, and therefore, a line with the shortest path among the following nodes is selected between the nodes with the direct connection relationship each time to calculate the shortest distance from the following node to the leader node.
and 140, clustering the following nodes to the selected leader node until all the following nodes are clustered.
In this embodiment, based on the first distance calculated in step 120, if the first distance from a following node to the ith leader node is the minimum (i =1,2, …, k), the following node is clustered to the leader node until all following nodes are clustered. Exemplary, such as: and only two leader nodes 1 and 2 are provided, the shortest distance from the follower node a to the leader node 1 is 10, and the shortest distance from the follower node a to the leader node 2 is 12, so that the follower nodes a are clustered to the leader node 1, that is, the follower nodes a and the leader node 1 are classified into one class. Similarly, by analogy, all the following nodes are clustered by adopting the method until all the following nodes are clustered, and all the following nodes can be understood as determining the corresponding leader nodes which belong to the same class as the following nodes.
According to the technical scheme, a leader node is determined in each node of the power system through a preset clustering numerical value, wherein nodes except the leader node in all the nodes are used as following nodes, a first preset algorithm is adopted to calculate the first distance from each following node to each leader node, one following node is selected, the leader node corresponding to the shortest distance in the first distances from the following nodes to each leader node is selected, and the following nodes are clustered to the selected leader node until all the following nodes are clustered. The flexibility, the operation efficiency and the response efficiency of the virtual power plant are improved, so that the dependence on the traditional power generation mode is reduced, and the economic cost and the environmental cost for maintaining the stability of the power system are reduced.
Fig. 2 is a schematic flow chart of another virtual power plant clustering method provided in an embodiment of the present invention, and referring to fig. 2, the method further includes the following steps:
in this embodiment, after the clustering is completed, the leader nodes of each class need to be determined again in each class. Illustratively, if there are 6 nodes in the power system, the node numbers are 1,2, 3, 4, 5 and 6, respectively, the leader node is 1 and 3, and the result after the clustering is 1,2 and 4, 3, 5 and 6.
Exemplarily, in the category 1,2 and 4, 1 is used as a sub-leader node, 2 and 4 are used as sub-follower nodes, and the 2, 4-node local agent executes a minimum consistency algorithm of discrete offsets to calculate a shortest distance to the sub-leader node 1, and if S21 and S41 are provided, S1= S21+ S41, where S21 and S41 are a second distance and S1 is a third distance, and the method for calculating S21 and S41 is the same as the method for calculating the first distance in step 120 in the foregoing embodiment; 2 is taken as a sub-leader node, 1 and 4 are taken as sub-follower nodes, the 1 and 4-node local agents execute a minimum consistency algorithm of discrete offset to calculate the shortest distance to the sub-leader node 2, and if the distance is S12 and S42, let S2= S12+ S42, where S12 and S42 are the second distance and S2 is the third distance; 4 is taken as a sub-leader node, 1 and 2 are taken as sub-follower nodes, the local agent of the 1 and 2 nodes executes a minimum consistency algorithm of discrete offset to calculate the shortest distance to the sub-leader node 4, and if the distance is S14 and S24, S4= S14+ S24, wherein S14 and S24 are second distances, and S4 is a third distance; based on the obtained S1, S2 and S4, the sizes of the three are compared, and if the S1 is the minimum, the node 1 is a class leader node in the classes of 1,2 and 4.
Similarly, in the classes 3, 5 and 6, the class leader node is determined by the same method as described above.
In this embodiment, after obtaining the third distance, the local agent of the node executes a discrete minimum consistency algorithm to find a node with a minimum distance value in each class, and the node serves as a new leader node, that is, a class leader node described herein, and broadcasts the new leader node to all nodes. Wherein the discrete minimum consistency algorithm is as follows:
after the broadcasting is finished, the discrete minimum consistency algorithm reaches a stable value, and the state values of the agents of each node are as follows:
X i (∞)＝min{x 1 (0),K,x i (0)，K,x n (0)}·1
wherein, X (∞) = [ X ] 1 (∞),K,x n (∞)] T ；1＝[1,K,1] T ，x i Is the state value of the node.
and 120, returning to the step of calculating the first distance from each following node to each leader node by adopting a first preset algorithm.
Further, if the class leader node is changed from the previous leader node, the class leader node is used as a new leader node, and the other nodes are used as follower nodes, and the process returns to step 120 until the class leader node is the same as the leader node, and the clustering is finished.
For example, the nodes 1 and 3 are assumed to be leader nodes, and if the node 1 is a class leader node in the classes 1,2 and 4 and the node 3 is a class leader node in the classes 3, 5 and 6, it is determined that clustering is finished; if the node 2 is a class leader node in the classes of 1,2 and 4 and the node 5 is a class leader node in the classes of 3, 5 and 6, taking the node 2 and the node 5 as leader nodes, returning to execute the step 120, and clustering again.
According to the technical scheme, after the clustering is finished, the leader node is determined again in each classification, and if the leader node is unchanged, the clustering is finished, so that the clustering accuracy is improved, and the clustering result is more in line with the actual situation.
Fig. 3 is a schematic flowchart of a leader node determining method according to an embodiment of the present invention, and referring to fig. 3, the method includes the following steps:
in this embodiment, if the preset clustering value is k, the preset centroid vector is a vector of k rows and 1 column, and if 1 node is selected as the leader node from beginning, the node number of the 1 node is stored in the preset centroid vector to indicate that a leader node is selected. The electrical characteristic parameter may be understood as a characteristic vector value in the above embodiment, and the local agent of the leader node executes a discrete maximum consistency algorithm to broadcast a preset centroid vector, so that each follower node knows that the 1 node is the leader node.
Wherein the discrete maximum consistency algorithm is as follows:
after the broadcasting is finished, the discrete maximum consistency algorithm reaches a stable value, and the state values of the agents of each node are as follows:
X i (∞)＝max{x 1 (0),K,x i (0)，K,x n (0)}·1
wherein X (∞) = [ X) 1 (∞),K,x n (∞)] T ；1＝[1,K,1] T 。
in this embodiment, the second preset algorithm is a discrete minimum consistency algorithm, which has been described in the above embodiment. Assuming that there are 6 nodes, the node numbers 1,2, 3, 4, 5 and 6,1 have been selected as leader nodes, and the electrical characteristic distance between two nodes connected in the network topology can be calculated, because the nodes 2, 3, 4, 5 and 6 are not necessarily directly connected in the network topology to the node 1, the shortest electrical characteristic distance between the nodes 2, 3, 4, 5 and 6 and the node 1, which has a direct connection line in the network topology, needs to be calculated. It is understood that 5 d are obtained i The value i denotes the index of the 2, 3, 4, 5, 6 nodes.
In the network topology diagram, if a line is used for connecting the node 1 and the node 2, it indicates that the node 1 and the node 2 are directly connected in the network topology.
the random number range is (0,d) i *d i ). Each following node generates a corresponding random number as long as the generated random number is (0,d) i *d i ) The preparation method is implemented by the following steps.
and comparing the random numbers generated by the following nodes, taking the following node corresponding to the maximum random number as a new leader node, and storing the new leader node in a preset centroid vector to show that one leader node is selected.
390, judging whether the number of the leader nodes reaches a preset clustering numerical value or not through a preset centroid vector;
3100, if so, judging that the leader node is completely acquired;
and 330, if the distance does not reach the preset distance, returning to the step of calculating the electrical characteristic distance from each following node to the leader node.
In this embodiment, after determining the new leader node and storing the new leader node in the preset centroid vector, the preset centroid vector is updated, and the updated preset centroid vector is broadcast to all nodes, so that other following nodes know which new centroid vector is. The preset centroid vector stores information of all leader nodes, so that the fact that the leader nodes are determined at present can be known through the preset centroid vector, and whether the appointed leader nodes are acquired can be known through comparison with the preset clustering value.
Further, if the number of the leader nodes determined at present reaches a preset clustering value, the leader nodes are judged to be completely acquired; otherwise, returning to the step of calculating the electrical characteristic distance from each following node to the leader node, and continuously acquiring the leader node.
The technical scheme of the embodiment describes a method for determining the leader node by using a distributed K-means + + algorithm, and the method introduces a distributed parallel computing mode on the basis of the traditional K-means + + algorithm, optimizes the method for determining the leader node, thereby reducing the total workload of distributed clustering computation and improving the clustering accuracy.
Fig. 4 is a schematic structural diagram of a clustering apparatus of a virtual power plant according to an embodiment of the present invention, where the apparatus is suitable for performing a clustering method of a virtual power plant according to any embodiment of the present invention, and as shown in fig. 4, the apparatus includes: a leader node obtaining module 410, a shortest distance determining module 420, a leader node selecting module 430 and a clustering module 440.
The leader node obtaining module 410 is configured to determine a leader node in each node of the power system according to a preset clustering value, where nodes except for the leader node in all nodes are used as follower nodes;
a shortest distance determining module 420, configured to respectively calculate first distances from the following nodes to the leader nodes by using a first preset algorithm;
a leader node selecting module 430, configured to select a following node optionally, and select a leader node corresponding to a shortest distance in first distances from the following node to each of the leader nodes;
and the clustering module 440 is configured to cluster the following nodes to the selected leader node until all the following nodes are clustered.
The clustering device for the virtual power plant provided by this embodiment determines a leader node in each node of a power system according to a preset clustering numerical value, wherein nodes except the leader node in all nodes are taken as following nodes, a first preset algorithm is adopted to calculate first distances from each following node to each leader node, one following node is selected, the leader node corresponding to the shortest distance in the first distances from the following node to each leader node is selected, and the following nodes are clustered to the selected leader node until all the following nodes are clustered. The flexibility, the operation efficiency and the response efficiency of the virtual power plant are improved, so that the dependence on the traditional power generation mode is reduced, and the economic cost and the environmental cost for maintaining the stability of the power system are reduced.
On the basis of the above embodiment, the method further comprises the following steps:
the class leader node acquisition module is used for acquiring class leader nodes in all the classes when the clustering is finished;
judging whether the class leader node is consistent with the leader node;
and if the two groups are consistent, judging that the clustering is finished.
On the basis of the above embodiment, the method further includes:
if the class leader nodes are not consistent, taking the class leader nodes as leader nodes, and taking the other nodes as follower nodes;
and returning to execute the step of respectively calculating the first distance from each following node to each leader node by adopting a first preset algorithm.
On the basis of the above embodiment, the class leader node acquiring module includes:
in each classification, each node is respectively used as a child leader node, and the other nodes are used as child follower nodes;
respectively calculating a second distance from each sub-following node to the corresponding sub-leader node by adopting a first preset algorithm;
calculating a sum of the second distances, and taking the sum as a third distance of the sub-leader node;
and comparing the third distances, and taking the node corresponding to the minimum third distance as a class leader node.
On the basis of the above embodiment, the leader node acquiring module 410 includes:
optionally selecting a leader node from all nodes of the power system and storing the leader node into a preset centroid vector, wherein the rest nodes are taken as follower nodes;
acquiring the electrical characteristic parameters of the leader node, and broadcasting the electrical characteristic parameters to the follower nodes;
calculating the electrical characteristic distance from each following node to the leader node;
calculating the shortest electrical characteristic distance of the network topology direct connection from each following node to the leader node by adopting a second preset algorithm;
the new leader node acquisition module is used for determining a new leader node according to the shortest electrical characteristic distance and storing the new leader node into the preset centroid vector;
updating the preset centroid vector, and broadcasting the updated preset centroid vector to all nodes;
judging whether the number of the leader nodes reaches a preset clustering value or not through a preset centroid vector;
if so, judging that the leader node is completely acquired;
and if not, returning to the step of calculating the electrical characteristic distance from each following node to the leader node.
On the basis of the above embodiment, the new leader node acquiring module includes:
determining a random number range according to the shortest electrical characteristic distance;
each following node generates a corresponding random number, wherein the generated random number is within the range of the random number;
and taking the following node corresponding to the generated maximum random number as a new leader node, and storing the new leader node in the preset centroid vector.
On the basis of the above embodiment, the first preset algorithm is a minimum consistency algorithm of discrete offsets; the second preset algorithm is a discrete minimum consistency algorithm.
Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a clustering method for a virtual power plant as provided in all embodiments of the present invention: that is, the program when executed by the processor implements: determining a leader node in each node of the power system through a preset clustering numerical value, wherein nodes except the leader node in all the nodes are taken as following nodes, a first preset algorithm is adopted to calculate the first distance from each following node to each leader node, one following node is selected, the leader node corresponding to the shortest distance in the first distances from the following node to each leader node is selected, and the following nodes are clustered to the selected leader node until all the following nodes are clustered.
Any combination of one or at least two computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or at least two wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or regulatory device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Fig. 5 is a schematic structural diagram of a regulation and control device provided in an embodiment of the present invention, where the regulation and control device may integrate the clustering device of the virtual power plant provided in the embodiment of the present invention. Referring to fig. 5, the conditioning apparatus 500 may include: a memory 510, a processor 520 and a computer program stored on the memory 510 and executable by the processor 520, wherein the processor 520 when executing the computer program implements a clustering method for a virtual power plant according to an embodiment of the present invention.
The regulation and control device provided by the embodiment of the invention determines the leader node in each node of the power system through a preset clustering numerical value, wherein the nodes except for the leader node in all the nodes are taken as following nodes, a first preset algorithm is adopted to respectively calculate the first distance from each following node to each leader node, one following node is selected, the leader node corresponding to the shortest distance in the first distances from the following nodes to each leader node is selected, and the following nodes are clustered to the selected leader node until all the following nodes are clustered. The flexibility, the operation efficiency and the response efficiency of the virtual power plant are improved, so that the dependence on the traditional power generation mode is reduced, and the economic cost and the environmental cost for maintaining the stability of the power system are reduced.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (9)
1. A clustering method of a virtual power plant is characterized by comprising the following steps:
determining a leader node in each node of the power system through a preset clustering value, wherein nodes except the leader node in all the nodes are taken as follower nodes;
respectively calculating a first distance from each following node to each leader node by adopting a first preset algorithm;
selecting one following node optionally, and selecting a leader node corresponding to the shortest distance from the following node to each leader node;
clustering the following nodes to the selected leader node until all the following nodes are clustered;
determining a leader node in each node of the power system through a preset clustering numerical value, wherein the method comprises the following steps:
optionally selecting a leader node from each node of the power system and storing a preset centroid vector, wherein the other nodes are taken as follower nodes;
acquiring the electrical characteristic parameters of the leader node, and broadcasting the electrical characteristic parameters to the follower nodes;
calculating the electrical characteristic distance from each following node to the leader node;
calculating the shortest electrical characteristic distance of the network topology direct connection from each following node to the leader node by adopting a second preset algorithm;
determining a new leader node according to the shortest electrical characteristic distance, and storing the new leader node in the preset centroid vector;
updating the preset centroid vector, and broadcasting the updated preset centroid vector to all nodes;
judging whether the number of the leader nodes reaches a preset clustering numerical value or not through a preset centroid vector;
if so, judging that the leader node is completely acquired;
and if not, returning to the step of calculating the electrical characteristic distance from each following node to the leader node.
2. The method of claim 1, wherein clustering the follower nodes to the selected leader node until all follower nodes are clustered further comprises:
when clustering is completed, obtaining class leader nodes in each class;
judging whether the class leader node is consistent with the leader node;
and if the two groups are consistent, judging that the clustering is finished.
3. The method of claim 2, wherein after determining whether the class leader node is consistent with the leader node, the clustering method of the virtual power plant further comprises:
if the class leader nodes are inconsistent, the class leader nodes are used as leader nodes, and the other nodes are used as follower nodes;
and returning to execute the step of respectively calculating the first distance from each following node to each leader node by adopting a first preset algorithm.
4. The method of claim 2, wherein obtaining the class leader node in each of the classifications upon completion of the clustering comprises:
in each classification, each node is respectively used as a child leader node, and the other nodes are used as child follower nodes;
respectively calculating a second distance from each sub-following node to the corresponding sub-leader node by adopting a first preset algorithm;
calculating a sum of the second distances, and taking the sum as a third distance of the sub-leader node;
and comparing the third distances, and taking the node corresponding to the minimum third distance as a class leader node.
5. The method of claim 1, wherein determining a new leader node from the shortest electrical signature distance and storing the preset centroid vector comprises:
determining a random number range according to the shortest electrical characteristic distance;
generating a corresponding random number by each following node, wherein the generated random number is in the range of the random number;
and taking the following node corresponding to the generated maximum random number as a new leader node, and storing the new leader node in the preset centroid vector.
6. The method according to any one of claims 1 to 5, wherein the first preset algorithm is a minimum consistency algorithm of discrete offsets; the second preset algorithm is a discrete minimum consistency algorithm.
7. A clustering device of a virtual power plant, comprising:
the leader node acquisition module is used for determining a leader node in each node of the power system through a preset clustering numerical value, wherein the nodes except the leader node in all the nodes are taken as following nodes;
the shortest distance determining module is used for respectively calculating first distances from the following nodes to the leader nodes by adopting a first preset algorithm;
the leader node selection module is used for selecting a following node optionally and selecting a leader node corresponding to the shortest distance in the first distances from the following node to the leader nodes;
the clustering module is used for clustering the following nodes to the selected leader node until all the following nodes are clustered; the leader node acquisition module comprises:
optionally selecting a leader node from each node of the power system and storing a preset centroid vector, wherein the other nodes are taken as follower nodes;
acquiring the electrical characteristic parameters of the leader node, and broadcasting the electrical characteristic parameters to the follower nodes;
calculating the electrical characteristic distance from each following node to the leader node;
calculating the shortest electrical characteristic distance of the network topology direct connection from each following node to the leader node by adopting a second preset algorithm;
the new leader node acquisition module is used for determining a new leader node according to the shortest electrical characteristic distance and storing the new leader node into the preset centroid vector;
updating the preset centroid vector, and broadcasting the updated preset centroid vector to all nodes;
judging whether the number of the leader nodes reaches a preset clustering numerical value or not through a preset centroid vector;
if so, judging that the leader node is completely acquired;
and if not, returning to the step of calculating the electrical characteristic distance from each following node to the leader node.
8. A computer storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the clustering method of a virtual power plant according to any of the claims 1-6.
9. A conditioning apparatus, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of clustering of virtual power plants according to any of claims 1 to 6 when executing the computer program.
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| CN111382939A (en) * | 2020-03-06 | 2020-07-07 | 国网冀北电力有限公司 | Virtual power plant resource optimal configuration method, device and equipment |
| CN111245026B (en) * | 2020-03-09 | 2021-10-19 | 国网冀北电力有限公司 | Virtual power plant regulation and control method, system and equipment |
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| CN113507141B (en) * | 2021-07-08 | 2024-04-09 | 国网上海市电力公司 | Virtual power plant equivalent closed-loop control method, system, electronic equipment and storage medium |
| CN114123175B (en) * | 2021-11-15 | 2023-11-07 | 东北大学 | Distributed control method for power distribution network considering generalized demand side resource alliance characteristics |
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