Scheduling method and scheduling device for power distribution network

Abstract

The invention provides a dispatching method and a dispatching device for a power distribution network. The method comprises the following steps: constructing a power distribution network adjacency matrix and a power distribution network weight matrix according to a topological structure of the power distribution network; dividing the power distribution network into a plurality of power distribution network communities according to the power distribution network weight matrix by adopting a Louvain community discovery algorithm to obtain a first community structure; establishing a constraint condition to optimize the first community structure to obtain a second community structure; constructing a communication network adjacent matrix of the communication network according to the hierarchical structure of the communication network; constructing a coupling network adjacency matrix according to the power distribution network adjacency matrix, the second community structure and the communication network adjacency matrix, wherein the coupling network adjacency matrix is used for describing a coupling relationship between the power distribution network and the communication network; and performing power dispatching on the power distribution network according to the coupling network adjacency matrix so as to balance power supply and demand of the power distribution network. The method solves the problem of low power dispatching efficiency caused by low coupling relationship between the power distribution network and the communication network in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of power grid dispatching, and in particular to a dispatching method and a dispatching device for a distribution network. Background Art

[0002] With the continuous development of smart grids, the intelligence, informationization and automation levels of power systems are constantly improving. A large number of advanced computer, communication and control technologies are applied to power grids. The number of information equipment such as sensors, actuators and communication equipment is growing rapidly, making the connection between information networks and power networks more and more complex. The modern power system has developed into a complex, multi-dimensional, heterogeneous power information-physical system formed by the mutual coupling of information systems (power communication networks) and physical systems (power grids). Among them, the traditional distribution network and the communication network are deeply integrated to form a distribution network information-physical system.

[0003] In recent years, photovoltaic, wind power distributed power sources, electric vehicles and distributed energy storage have been connected to the distribution network in large numbers. Due to their characteristics of randomness, volatility and intermittency, the difficulty of distribution network state estimation and situation awareness has increased. The distribution network faces challenges such as load imbalance, difficult dispatching and control, and reduced network robustness. In addition, it has not established a good connection with the communication network. Therefore, it is necessary to study the information-physical system architecture of the distribution network, analyze the interdependence and coupling process between the distribution network and the communication network, and provide a theoretical basis for exploring dispatching and control strategies and the robustness of the distribution network. Summary of the invention

[0004] The main purpose of the present application is to provide a dispatching method and a dispatching device for a distribution network, so as to at least solve the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a dispatching method for a distribution network is provided, comprising: constructing a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between each of the nodes in the distribution network; using the Louvain community discovery algorithm to divide the distribution network into multiple distribution network communities according to the distribution network weight matrix, and obtain a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two of the nodes in the distribution network community is greater than a predetermined threshold, and the first community structure is each of the distribution network communities in the distribution network. The structural domain is composed of the electrical distance being the change in voltage amplitude or phase angle between each of the nodes; establishing constraints to optimize the first community structure to obtain a second community structure so that the second community structure satisfies the constraints; constructing a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, the communication network adjacency matrix being used to describe the connection relationship between each information node in the communication network; constructing a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure and the communication network adjacency matrix, the coupling network adjacency matrix being used to describe the coupling relationship between the distribution network and the communication network; and performing power dispatch on the distribution network according to the coupling network adjacency matrix so as to balance the power supply and demand of the distribution network.

[0006] Optionally, constructing a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network includes: constructing a distribution network undirected graph G of the distribution network according to the topological structure of the distribution network p =(V p ,E p ,W p ), where V p is the set of nodes in the distribution network, E p is the set of edges in the distribution network, W p is a set of weights of the edges in the distribution network, the nodes are power equipment in the distribution network, the edges are lines connecting the nodes in the distribution network, and the weights of the edges are the electrical coupling strengths between the nodes; the connection relationship between the nodes is determined according to the topological structure of the distribution network to obtain the adjacency matrix of the distribution network Among them, a ij For node v i With node v j The connection relationship between them, n is the total number of nodes in the distribution network; the distribution network weight matrix is ​​obtained according to the electrical distance calculation Among them, w ij For node vi With node v j The size of the weight of the edge between the nodes, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between the nodes.

[0007] Optionally, the distribution network weight matrix is ​​obtained according to the electrical distance calculation, including: using active sensitivity and reactive sensitivity to represent the electrical distance, the active sensitivity is the influence of the active power change of the node on the voltage amplitude or phase angle change of other nodes, and the reactive sensitivity is the influence of the reactive power change of the node on the voltage amplitude or voltage phase angle change of other nodes; constructing a sensitivity matrix according to the active sensitivity and the reactive sensitivity Among them, S Pδ is the phase angle active power matrix, which is used to describe the influence of the change of active power ΔP of each node on the phase angle Δδ of the corresponding node, S PV is the voltage amplitude active matrix, which is used to describe the influence of the change of active power ΔP of each node on the voltage amplitude ΔV of the corresponding node, S Qδ is the phase angle reactive matrix, which is used to describe the influence of the change of reactive power ΔP of each node on the phase angle Δδ of the corresponding node, S QV is a voltage amplitude reactive matrix, which is used to describe the influence of the change in reactive power ΔP of each node on the voltage amplitude ΔV of the corresponding node. The sensitivity matrix is ​​used to describe the influence relationship between the power change and the voltage change of each node in the distribution network. The distribution network weight matrix is ​​calculated based on the sensitivity matrix.

[0008] Optionally, calculating the distribution network weight matrix according to the sensitivity matrix includes: calculating the reactive electrical distance between each node in the distribution network according to the sensitivity matrix in, i=1, 2…n, j=1, 2…n, When node v j When the reactive power changes, the node v j With node v i Ratio of voltage amplitude change, S QV,ij is the element in the i-th row and j-th column of the voltage amplitude reactive matrix, representing the node v j Reactive power change to node v i The impact of voltage amplitude change, ΔV Qj For node v j The influence of reactive power variation on voltage amplitude variation; the active electrical distance between each node in the distribution network is calculated according to the sensitivity matrix in, When node vj When the active power changes, the node v j With node v i Ratio of voltage amplitude change, S PV,ij is the element in the i-th row and j-th column of the voltage amplitude active matrix, representing the node v j Active power change to node v i The impact of voltage amplitude change, ΔV Pj For node v j The influence of the active power change on the voltage amplitude change; the electrical distance between each node is obtained according to the reactive electrical distance and the active electrical distance: The elements w in the distribution network weight matrix are obtained by calculating the electrical distance ij =(1-L ij ) / max(L).

[0009] Optionally, the distribution network is divided into a plurality of distribution network communities according to the distribution network weight matrix using the Louvain community discovery algorithm to obtain a first community structure, including: taking each node in the distribution network as a community to obtain a current community of each node; a first allocation step for allocating any target node among the nodes to the current community corresponding to other nodes having a connection relationship with the target node, and calculating the modularity gain of each current community. Among them, Σ in is the sum of the weights of all the edges in the current community, Σ tot is the sum of the weights of the edges connecting the nodes in the current community and other communities, k i For node v i The sum of the weights of the edges connecting the nodes, k i,in For node v iThe sum of the weights of the edges within the current community, m is the sum of the weights of all the edges in the distribution network; a first determination step is used to determine the current community with the largest modularity gain according to the modularity gains of each of the current communities, and determine it as the maximum gain community; a second allocation step is used to allocate the target node to the maximum gain community when the modularity gain of the maximum gain community is greater than 0, and update the nodes included in the current maximum gain community; a third allocation step is used to allocate the target node when the modularity gain of the community with the largest modularity gain is less than 0; a first repetition step is used to repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence until each of the nodes is no longer allocated, and each of the current communities is determined as a super node at this time, and a target network structure is formed according to the super nodes; a second repetition step is used to repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence until the modularity gain of each of the current communities in the target network structure no longer changes, and a first community structure is obtained.

[0010] Optionally, constraints are established to optimize the first community structure to obtain a second community structure, including: a first constraint for setting that each community in the first community structure includes at least one power node and one load node; a second constraint for setting the number of distribution network communities in the first community structure max (N min ,N f -2)≤n≤min(N max ,N f ), where Nmin is the minimum number threshold of the distribution network community in the first community structure, N max is the maximum number threshold of the distribution network community in the first community structure, N f is the cube root of the total number of the nodes in the distribution network; the third constraint is used to set the ratio of the total power of the load nodes in each of the communities in the first community structure to the total power of the power supply nodes between 0.6 and 1.5; the first community structure is optimized according to the first constraint, the second constraint and the third constraint to obtain the second community structure.

[0011] Optionally, constructing a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, including: dividing the communication network into an access layer, a backbone layer and a core layer according to the hierarchical structure of the communication network, the access layer is used to collect information of the distribution network and transmit the dispatch control signal, the backbone layer is used for interconnection between communities and forwarding information from the access layer, the core layer is interconnected with the regional control center in the backbone layer, including a main dispatch center and a backup dispatch center, and the core layer is used for information processing; constructing a communication network undirected graph G according to the network structure of the communication network c =(V c ,E c ), where V c is a collection of nodes in the communication network, including information nodes in the access layer, regional control nodes in the backbone layer, and dispatch center nodes in the core layer. c is the set of edges in the communication network; determining the number of information nodes and the number of regional control nodes in the communication network, the number of information nodes in the communication network is equal to the number of nodes in the distribution network, and the number of regional control nodes is equal to the number of distribution network communities in the second community structure in the distribution network; adding the information nodes in the communication network based on the turntable idea to form a final communication network community, the final communication network community is the area formed by the information nodes and the connection relationship between each information node, and the information nodes correspond one-to-one to each node in the distribution network; obtaining the communication network adjacency matrix according to the final communication network community Wherein, m is the total number of the information nodes.

[0012] Optionally, based on the turntable idea, the information node is added to the communication network to form a final communication network community, including: constructing a community priority selection probability function Among them, C i is the number of nodes in the ith communication network community, and the communication network community corresponds one-to-one to the distribution network community; constructing a node priority connection probability function Among them, δ Ai is the probability of node i being selected in the Ath communication network community, k Ai is the number of edges of node i in the Ath communication network community, k max is the maximum value of the number of the edges of the node in the communication network community; and constructs the final communication network community according to the community priority selection probability function and the node priority connection probability function.

[0013] Optionally, constructing a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix includes: constructing the coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix Among them, A p-c It is an association matrix, which is used to describe the coupling relationship between the nodes in the distribution network and the nodes in the communication network.

[0014] According to another aspect of the present application, a dispatching device for a distribution network is provided, comprising: a first establishing unit, configured to construct a distribution network adjacency matrix and a distribution network weight matrix according to a topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between each of the nodes in the distribution network; a first dividing unit, configured to divide the distribution network into a plurality of distribution network communities according to the distribution network weight matrix using a Louvain community discovery algorithm to obtain a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two of the nodes in the distribution network community is greater than a predetermined threshold, and the first community structure is a structural domain composed of each of the distribution network communities in the distribution network, and the electrical distance is the change in voltage amplitude or phase angle between each of the nodes; a first constraint unit is used to establish constraint conditions to optimize the first community structure to obtain a second community structure so that the second community structure satisfies the constraint conditions; a second establishment unit is used to construct a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, and the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network; a second establishment unit is used to establish a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, and the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network; a first control unit is used to perform power dispatch on the distribution network according to the coupling network adjacency matrix to balance the power supply and demand of the distribution network.

[0015] Applying the technical solution of the present application, in the above-mentioned distribution network dispatching method, it includes: constructing a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, the above-mentioned distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the above-mentioned distribution network, and the above-mentioned distribution network weight matrix is ​​used to describe the electrical coupling strength between each of the above-mentioned nodes in the above-mentioned distribution network; using the Louvain community discovery algorithm to divide the above-mentioned distribution network into multiple distribution network communities according to the above-mentioned distribution network weight matrix, to obtain a first community structure, the above-mentioned distribution network community is a part of the above-mentioned distribution network, and the electrical distance between any two of the above-mentioned nodes in the above-mentioned distribution network community is greater than a predetermined threshold, and the above-mentioned first community structure is a structure composed of each of the above-mentioned distribution network communities in the above-mentioned distribution network. domain, the electrical distance is the change in voltage amplitude or phase angle between each of the above nodes; constraints are established to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraints; a communication network adjacency matrix of the communication network is constructed according to the hierarchical structure of the communication network, and the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network; a coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, and the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network; power dispatch is performed on the distribution network according to the coupling network adjacency matrix to balance the power supply and demand of the distribution network. The present application obtains the electrical coupling strength of each node in the distribution network by constructing a distribution network adjacency matrix and a distribution network weight matrix, divides each node in the distribution network into communities according to the Louvain community discovery algorithm, and constructs a communication network adjacency matrix according to the hierarchical structure of the communication network. The coupling relationship between the distribution network and the communication network is established through the distribution network adjacency matrix and the communication network adjacency matrix. According to the coupling relationship, dispatchers can make better decisions on the power dispatching of the distribution network, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal according to a method for dispatching a distribution network provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic diagram of a flow chart of a method for dispatching a distribution network provided according to an embodiment of the present application is shown;

[0018] Figure 3 A structural block diagram of a dispatching device for a distribution network provided according to an embodiment of the present application is shown.

[0019] The above drawings include the following reference numerals:

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background technology, the distribution network in the prior art faces the problems of load imbalance, difficult scheduling and control, low network robustness, and has not established a good connection with the communication layer. In order to solve this technical problem, the embodiments of the present application provide a scheduling method and scheduling device for a distribution network.

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for dispatching a distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for dispatching a distribution network in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a dispatching method for a distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as 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 executed in an order different from that shown here.

[0029] Figure 2 1 is a flow chart of a method for dispatching a distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0030] Step S201, constructing a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between the nodes in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between the nodes in the distribution network;

[0031] Specifically, the distribution network adjacency matrix and distribution network weight matrix are constructed according to the topological structure of the distribution network. The distribution network adjacency matrix describes the connection relationship between each node in the distribution network, which can more clearly understand whether the nodes in the distribution network are connected. The distribution network weight matrix describes the electrical coupling strength between each node in the distribution network, which can more clearly understand the degree of coupling and correlation strength between each node. This expression method is helpful for analyzing and optimizing the topological structure and electrical characteristics of the distribution network.

[0032] Step S202, using the Louvain community discovery algorithm to divide the distribution network into a plurality of distribution network communities according to the distribution network weight matrix, and obtaining a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two of the nodes in the distribution network community is greater than a predetermined threshold, and the first community structure is a structural domain composed of the distribution network communities in the distribution network, and the electrical distance is a change in the voltage amplitude or phase angle between the nodes;

[0033] Specifically, according to the weight matrix of the distribution network, the Louvain community discovery algorithm is used to divide the distribution network into communities, and a first community structure is obtained. In this community structure, the distribution network is divided into multiple communities, each of which is a part of the distribution network. In the first community structure, the electrical distance between any two nodes is greater than a predetermined threshold, where the electrical distance represents the change in the voltage amplitude or phase angle between the nodes. The effect of achieving this community division is to better understand the relationship and characteristics between nodes in the distribution network, and to cluster nodes with similar electrical characteristics together to form communities.

[0034] Step S203, establishing constraint conditions to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint conditions;

[0035] Specifically, the first community structure is adjusted according to the constraints to form a second community structure to ensure that the second community structure meets the pre-set conditions. This optimization process can help optimize the structure of the distribution network, making the community structure more in line with the needs and helping to improve the reliability of the distribution network.

[0036] Step S204, constructing a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, wherein the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network;

[0037] Specifically, the communication network adjacency matrix of the communication network is constructed according to the hierarchical structure of the communication network, which can be used to describe the connection relationship between each information node in the communication network. Through the adjacency matrix, the association relationship between the information nodes in the communication network can be clearly displayed, which is helpful for analyzing and understanding the topological structure of the communication network, and can intuitively display the connection relationship between each information node in the communication network, as well as the hierarchical structure between the nodes.

[0038] Step S205, constructing a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, wherein the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network;

[0039] Specifically, a coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure and the communication network adjacency matrix. It is used to describe the coupling relationship between the distribution network and the communication network, and to represent the mutual influence and association between the distribution network and the communication network. It helps to understand the interaction and influence between the two and helps to optimize the coordinated operation and management of the two network systems.

[0040] Step S206: performing power dispatching on the distribution network according to the coupling network adjacency matrix to balance the power supply and demand of the distribution network.

[0041] Specifically, the coupled network adjacency matrix is ​​used to guide the power dispatching of the distribution network to ensure that the power supply capacity of the distribution network can meet the power demand. Through power dispatching, the operation of the distribution network can be optimized so that the supply and demand in the power system can be balanced. With the guidance of the coupled network adjacency matrix, the distribution network can be reasonably dispatched and the power distribution can be adjusted to meet the power needs of different regions and users.

[0042] Through this embodiment, in the above-mentioned distribution network dispatching method, a distribution network adjacency matrix and a distribution network weight matrix are constructed according to the topological structure of the distribution network, the above-mentioned distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the above-mentioned distribution network, and the above-mentioned distribution network weight matrix is ​​used to describe the electrical coupling strength between each of the above-mentioned nodes in the above-mentioned distribution network; the above-mentioned distribution network is divided into multiple distribution network communities according to the above-mentioned distribution network weight matrix by using the Louvain community discovery algorithm to obtain a first community structure, the above-mentioned distribution network community is a part of the above-mentioned distribution network, and the electrical distance between any two of the above-mentioned nodes in the above-mentioned distribution network community is greater than a predetermined threshold, and the above-mentioned first community structure is a structural domain composed of each of the above-mentioned distribution network communities in the above-mentioned distribution network. The electrical distance is the change in voltage amplitude or phase angle between the nodes. Constraints are established to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraints. A communication network adjacency matrix is ​​constructed according to the hierarchical structure of the communication network. The communication network adjacency matrix is ​​used to describe the connection relationship between the information nodes in the communication network. A coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure and the communication network adjacency matrix. The coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network. The distribution network is dispatched according to the coupling network adjacency matrix to balance the supply and demand of power in the distribution network. The present application obtains the electrical coupling strength of each node in the distribution network by constructing a distribution network adjacency matrix and a distribution network weight matrix, divides each node in the distribution network into communities according to the Louvain community discovery algorithm, and constructs a communication network adjacency matrix according to the hierarchical structure of the communication network. The coupling relationship between the distribution network and the communication network is established through the distribution network adjacency matrix and the communication network adjacency matrix. According to the coupling relationship, dispatchers can make better decisions on the power dispatching of the distribution network, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art.

[0043] In order to obtain the distribution network adjacency matrix and the distribution network weight matrix, in an optional implementation, the distribution network adjacency matrix and the distribution network weight matrix are constructed according to the topological structure of the distribution network. The above step S201 includes:

[0044] Step S2011, constructing a distribution network undirected graph G of the distribution network according to the topological structure of the distribution network p =(V p ,E p ,W p ), where V p is the set of nodes in the above distribution network, E p is the set of edges in the above distribution network, W pis a set consisting of the weights of the edges in the distribution network, the nodes are the power equipment in the distribution network, the edges are the lines connecting the nodes in the distribution network, and the weights of the edges are the electrical coupling strengths between the nodes;

[0045] Specifically, an undirected graph is constructed according to the topological structure of the distribution network, in which the nodes represent the power equipment in the distribution network, the edges represent the lines connecting these devices, and the edge weights represent the strength of the electrical coupling between the devices. This graph representation helps to intuitively display the relationship and connection between the devices in the distribution network, facilitating network analysis and optimization.

[0046] Step S2012: determine the connection relationship between the nodes according to the topological structure of the distribution network to obtain the adjacency matrix of the distribution network. Among them, a ij For node v i With node v j The connection relationship between them, n is the total number of nodes in the above distribution network;

[0047] Specifically, the connection between nodes in the distribution network is described by the distribution network adjacency matrix, where each element represents the connection relationship between nodes. i With node v j If there is a connection between them, the value of the element of the corresponding distribution network adjacency matrix is ​​1; if there is no connection, the value is 0. The distribution network adjacency matrix can clearly show the connection relationship between the nodes in the distribution network.

[0048] Step S2013, calculate the distribution network weight matrix based on the electrical distance Among them, w ij For node v i With node v j The weights of the above edges are determined by the weight matrix of the distribution network, and the above distribution network weight matrix is ​​used to describe the electrical coupling strength between the above nodes.

[0049] Specifically, the distribution network weight matrix is ​​calculated based on the electrical distance, which describes the electrical coupling strength between nodes. Each element represents the weight of the edge between nodes, that is, a measure of the electrical coupling strength. This matrix helps to analyze the degree of association and influence between nodes in the distribution network. The effect of this expression is that the electrical coupling strength between nodes in the distribution network can be clearly described through the weight matrix.

[0050] In order to determine the distribution network weight matrix through the sensitivity matrix, in an optional implementation, the distribution network weight matrix is ​​obtained according to the electrical distance calculation, and the step S2013 includes:

[0051] Step S20131, using active sensitivity and reactive sensitivity to represent the electrical distance, the active sensitivity being the influence of active power change of a node on voltage amplitude or phase angle change of other nodes, and the reactive sensitivity being the influence of reactive power change of a node on voltage amplitude or voltage phase angle change of other nodes;

[0052] Specifically, active sensitivity and reactive sensitivity are used to represent the electrical distance, where active sensitivity represents the impact of changes in active power of a node on changes in voltage amplitude or phase angle of other nodes, and reactive sensitivity represents the impact of changes in reactive power of a node on changes in voltage amplitude or phase angle of other nodes. By representing active and reactive sensitivities, the coupling relationship and electrical distance between nodes can be more accurately described, helping to understand and optimize the electrical characteristics between different nodes in the distribution network.

[0053] Step S20132: construct a sensitivity matrix based on the active sensitivity and the reactive sensitivity. Among them, S Pδ is the phase angle active matrix, which is used to describe the impact of the change in active power ΔP of each of the above nodes on the phase angle Δδ of the corresponding node, S PV is the voltage amplitude active matrix, which is used to describe the influence of the change in active power ΔP of each of the above nodes on the voltage amplitude ΔV of the corresponding node, S Qδ is the phase angle reactive matrix, which is used to describe the influence of the change of reactive power ΔP of each node on the phase angle Δδ of the corresponding node. QV is a voltage amplitude reactive matrix, which is used to describe the influence of the change in reactive power ΔP of each of the above nodes on the voltage amplitude ΔV of the corresponding node. The above sensitivity matrix is ​​used to describe the influence relationship between the power change and the voltage change of each of the above nodes in the above distribution network.

[0054] Specifically, a sensitivity matrix is ​​constructed based on active and reactive sensitivities, which includes phase angle active matrix, voltage amplitude active matrix, phase angle reactive matrix and voltage amplitude reactive matrix. These matrices describe the influence of node power changes on the corresponding node phase angle and voltage amplitude. Through the sensitivity matrix, the correlation between node power changes and voltage changes can be quantified, and the mutual influence and coupling degree between nodes in the power system can be understood.

[0055] Step S20133, calculating the distribution network weight matrix based on the sensitivity matrix.

[0056] Specifically, the sensitivity matrix is ​​used to calculate the distribution network weight matrix representing the electrical coupling strength between nodes in the distribution network. By calculating the sensitivity matrix, the impact of node power changes on voltage changes can be quantified, thereby determining the coupling strength between nodes and forming a weight matrix.

[0057] In order to determine each element in the distribution network weight matrix, in an optional implementation, the distribution network weight matrix is ​​calculated according to the sensitivity matrix, and the step S20133 includes:

[0058] Step S201331, calculating the reactive electrical distance between each of the above nodes in the above distribution network according to the above sensitivity matrix in, When node v j When the reactive power changes, the node v j With node v i Ratio of voltage amplitude change, S QV,ij is the element in the i-th row and j-th column of the voltage amplitude reactive matrix above, representing the node v j Reactive power change to node v i The impact of voltage amplitude change, ΔV Qj For node v j The impact of reactive power changes on voltage amplitude changes;

[0059] Specifically, the reactive electrical distance between nodes in the distribution network is calculated using the sensitivity matrix, which takes into account the impact of reactive power changes on voltage amplitude changes. By calculating the reactive electrical distance, the impact of reactive power changes between nodes on voltage amplitude changes can be evaluated, and the correlation between reactive power changes and voltage amplitude changes between nodes can be quantified, which helps to more accurately evaluate and manage the mutual impact of reactive power between nodes in the distribution network.

[0060] Step 201332, calculate the active electrical distance between each of the above nodes in the above distribution network according to the above sensitivity matrix in, When node v j When the active power changes, the node v j With node v i Ratio of voltage amplitude change, S PV,ij is the element in the i-th row and j-th column of the voltage amplitude active matrix above, representing the node v j Active power change to node v i The impact of voltage amplitude change, ΔV Pj For node v j The impact of active power changes on voltage amplitude changes;

[0061] Specifically, the sensitivity matrix is ​​used to calculate the active electrical distance between nodes in the distribution network, which takes into account the impact of active power changes on voltage amplitude changes. By calculating the active electrical distance, the impact of active power changes between nodes on voltage amplitude changes can be evaluated, and the correlation between active power changes and voltage amplitude changes between nodes can be quantified, which helps to more accurately evaluate and manage the mutual influence of active power between nodes in the distribution network.

[0062] Step S201333, according to the reactive electrical distance and the active electrical distance, the electrical distance between each of the nodes is obtained as follows:

[0063] Specifically, the electrical distance between nodes is calculated by comprehensively considering the reactive electrical distance and the active electrical distance. This method integrates the impact of node power changes on voltage changes and quantifies the electrical coupling relationship between nodes.

[0064] Step S201334, calculating the elements w in the distribution network weight matrix according to the electrical distance ij =(1-L ij ) / max(L).

[0065] Specifically, the value of each element in the weight matrix of the distribution network is obtained by calculating the electrical distance. According to the calculation results of the electrical distance, the coupling degree between the nodes in the weight matrix is ​​determined, providing quantitative information about the interaction between the nodes.

[0066] In order to obtain the first community structure, in an optional implementation, the Louvain community discovery algorithm is used to divide the distribution network into multiple distribution network communities according to the distribution network weight matrix to obtain the first community structure. The step S202 includes:

[0067] Step S2021, taking each node in the above distribution network as a community, and obtaining the current community of each of the above nodes;

[0068] Specifically, each node in the distribution network is regarded as a community, and the current community of each node is obtained. This approach regards each node as a whole, emphasizes the integrity of the node, and provides a basis for the subsequent establishment of the community.

[0069] Step S2022, the first allocation step, is used to allocate any target node among the above nodes to the above current community corresponding to other nodes that have a connection relationship with the above target node, and calculate the modularity gain of each of the above current communities Among them, Σ in is the sum of the weights of all the above edges in the current community, Σ totis the sum of the weights of the edges connecting the nodes in the current community and other communities, k i For node v i The sum of the weights of the above edges of the connected nodes, k i,in For node v i The sum of the weights of the above edges within the current community, m is the sum of the weights of all the above edges in the above distribution network;

[0070] Specifically, in the first allocation step, each target node is allocated to other nodes in the current community that have a connection relationship with it, and then the modularity gain of the current community is calculated. The modularity gain is used to measure the connection strength within the community and the connection strength between communities. The calculation of modularity gain provides a quantitative indicator for community optimization, which helps guide community allocation and structural adjustment.

[0071] Step S2023, a first determination step, for determining the current community with the largest modularity gain according to the modularity gains of the current communities, and determining it as the maximum gain community;

[0072] Specifically, in the first determination step, the modularity gains of various current communities are compared, and the current community with the maximum modularity gain is determined and determined as the maximum gain community. Determining the maximum gain community helps to identify the most optimized part of the community structure, making the internal connection of the community closer.

[0073] Step S2024, a second allocation step, in the case where the modularity gain of the maximum gain community is greater than 0, the target node is allocated to the maximum gain community, and the nodes included in the current maximum gain community are updated;

[0074] Specifically, in the second allocation step, if the modularity gain of the maximum gain community is greater than 0, the target node is allocated to the maximum gain community, and the nodes included in the current maximum gain community are updated, thereby optimizing the community structure and improving the compactness and efficiency of the community.

[0075] Step S2025, a third allocation step, in which, when the modularity gain of the community with the largest modularity gain is less than 0, the target node is not allocated;

[0076] Specifically, in the third allocation step, if the modularity gain of the community with the largest modularity gain is less than 0, the target node will not be allocated. The purpose of this step is to consider that the structure of the community is not ideal or optimized when the modularity gain of the community with the largest modularity gain is negative, and therefore choose not to allocate the target node.

[0077] Step S2026, a first repetition step, repeating the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence, until each of the above nodes is no longer allocated, and determining each of the above current communities as a super node, and forming a target network structure according to the super nodes;

[0078] Specifically, in the first repetitive step, the first allocation step, the first determination step, the second allocation step and the third allocation step are executed cyclically at least once until all nodes are no longer allocated. At this time, each current community is determined to be a super node. This iterative process continuously optimizes the community allocation and eventually forms a new network structure, in which the super node represents an optimized set of nodes. Through iterative cycles, the community structure is continuously optimized and adjusted to improve the correlation between nodes.

[0079] Step S2027, the second repetition step, repeats the first allocation step, the first determination step, the second allocation step, and the third allocation step in sequence at least once, until the modularity gain of each of the current communities in the target network structure no longer changes, and a first community structure is obtained.

[0080] Specifically, in the second repetitive step, the first allocation step, the first determination step, the second allocation step and the third allocation step are repeated in sequence until the modularity gain of each current community in the target network structure no longer changes, thereby forming a first community structure. Through multiple iterative repetitive steps, the community structure can be effectively optimized to make it more adaptable to and optimize the needs of the distribution network, thereby improving the reliability of the system.

[0081] In order to obtain the second community structure, in an optional implementation, a constraint condition is established to optimize the first community structure to obtain the second community structure. The step S203 includes:

[0082] Step S2031, a first constraint condition is used to set each of the above communities in the above first community structure to include at least one power node and one load node;

[0083] Specifically, the first constraint condition stipulates that each community in the first community structure contains at least one power node and one load node. This constraint condition ensures that there are power nodes and load nodes in each community to maintain the power supply and load balance of the distribution network and ensure the normal operation and stability of the power system.

[0084] Step S2032, the second constraint condition is used to set the number of the distribution network communities in the first community structure max (N min ,N f -2)≤n≤min(N max ,N f), where Nmin is the minimum number threshold of the distribution network community in the first community structure, N max is the maximum number threshold of the distribution network community in the first community structure, N f is the cube root of the total number of the above nodes in the above distribution network;

[0085] Specifically, the second constraint stipulates the number of distribution network communities in the first community structure. Such a constraint helps to control the number of communities and ensure the rationality and effectiveness of the community structure. Too many communities may make the model too complex and increase the computational burden; too few communities may make the model too rough and unable to accurately reflect the local characteristics of the network.

[0086] Step S2033, a third constraint condition is used to set the ratio of the total power of the load nodes in each of the communities in the first community structure to the total power of the power source nodes to be between 0.6 and 1.5;

[0087] Specifically, the third constraint stipulates that the ratio of the total power of the load nodes to the total power of the power supply nodes of each community in the first community structure should be between 0.6 and 1.5. This constraint helps to maintain the supply and demand balance within each community and ensure a reasonable match between load and power supply, thereby ensuring the stable operation of the distribution network and the balance of power supply load.

[0088] Step S2034: Optimize the first community structure according to the first constraint condition, the second constraint condition and the third constraint condition to obtain a second community structure.

[0089] Specifically, by optimizing the community structure that meets the first, second and third constraints, an adjusted and improved second community structure is obtained, which makes the second community structure more reasonable and optimized while meeting the constraints. The optimized second community structure helps to maintain the balance between supply and demand and the reasonable connection relationship between communities.

[0090] In order to obtain the communication network adjacency matrix, in an optional implementation, the communication network adjacency matrix of the communication network is constructed according to the hierarchical structure of the communication network, and the step S204 includes:

[0091] Step S2041, dividing the communication network into an access layer, a backbone layer and a core layer according to the hierarchical structure of the communication network, the access layer is used to collect information of the distribution network and transmit the dispatch control signal, the backbone layer is used for interconnection between communities and forwarding information from the access layer, the core layer is interconnected with the regional control center in the backbone layer, including the main dispatch center and the backup dispatch center, and the core layer is used for information processing;

[0092] Specifically, according to the hierarchical structure of the communication network, the communication network can be divided into an access layer, a backbone layer and a core layer. The access layer is mainly responsible for collecting distribution network information and transmitting control signals. The backbone layer is mainly responsible for the connection between communities and information forwarding. The core layer connects the regional control center, including the main dispatching center and the backup dispatching center, and is responsible for information processing. This hierarchical design ensures the transmission of information flow and realizes efficient collaboration between the physical and information levels in the smart grid.

[0093] Step S2042: construct a communication network undirected graph G according to the network structure of the communication network c =(V c ,E c ), where V c is a collection of nodes in the above communication network, including the information nodes in the above access layer, the regional control nodes in the above backbone layer, the dispatch center nodes in the above core layer, c is the set of the above edges in the above communication network;

[0094] Specifically, based on the hierarchical architecture of the communication network, an undirected graph model is constructed, whose node set covers the information nodes of the access layer, the regional control nodes of the backbone layer, and the scheduling center nodes of the core layer, and the edge set defines the interconnection relationship between the nodes.

[0095] Step S2043, determining the number of the information nodes and the number of the regional control nodes in the communication network, the number of the information nodes in the communication network being equal to the number of nodes in the distribution network, and the number of the regional control nodes being equal to the number of distribution network communities in the second community structure in the distribution network;

[0096] Specifically, it ensures that the number of information nodes in the communication network corresponds one-to-one with the nodes in the distribution network. At the same time, the number of deployed regional control nodes also accurately matches the number of communities in the distribution network.

[0097] Step S2044, based on the turntable concept, the information nodes are added to the communication network to form a final communication network community, where the final communication network community is an area formed by the information nodes and the connection relationship between the information nodes, and the information nodes correspond one-to-one to the nodes in the distribution network;

[0098] Specifically, using the random selection mechanism of the turntable, we integrate information nodes into the communication network to form a final communication community structure that is directly mapped to the distribution network nodes. Through this node addition strategy and community construction method, we achieve precise docking between the communication network and the distribution network.

[0099] Step S2045, obtaining the communication network adjacency matrix according to the final communication network community Wherein, m is the total number of the above information nodes.

[0100] Specifically, according to the final communication network community we constructed, we determine the communication network adjacency matrix. Through the communication network adjacency matrix, we can determine the association relationship between each information node and establish a good connection for each information node.

[0101] In order to obtain the final communication network community, in an optional implementation, based on the turntable concept, the above information nodes are added to the above communication network to form the final communication network community. The above step S2044 includes:

[0102] Step S20441, constructing a community priority selection probability function Among them, C i is the number of nodes in the ith communication network community, and the above communication network community corresponds one-to-one to the above distribution network community;

[0103] Specifically, through the community priority selection probability function, the communication network community can be given priority. The calculation method of this probability function is determined according to the number of nodes in each communication network community. Each communication network community corresponds to a distribution network community. When selecting a community, the number of nodes in the communication network community is given priority to ensure that the communication needs can be better met when building the network.

[0104] Step S20442: Constructing a node priority connection probability function Among them, δ Ai is the probability of node i being selected in the Ath communication network community, k Ai is the number of the above edges of node i in the Ath communication network community, k max is the maximum number of the above edges of the above nodes in the communication network community;

[0105] Specifically, constructing a node priority connection probability function can make the priority of node connection more reasonable and efficient. By considering the number of edges of the node and the maximum number of node edges in the entire community, the importance of the node and the closeness of the connection can be better measured, thereby achieving a more effective node connection strategy.

[0106] Step S20443, constructing the above-mentioned final communication network community according to the above-mentioned community priority selection probability function and the above-mentioned node priority connection probability function.

[0107] Specifically, through the community priority selection probability function and the node priority connection probability function, we constructed a final communication network community. The two functions can effectively organize nodes into communities, thereby improving the efficiency and performance of the network and forming a closer community structure.

[0108] In order to obtain the coupling network adjacency matrix, in an optional implementation, the coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. The step S205 includes:

[0109] Step S2051, constructing the coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix Among them, A p-c It is an association matrix, which is used to describe the coupling relationship between the nodes in the distribution network and the nodes in the communication network.

[0110] Specifically, a coupling network adjacency matrix was constructed based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. This coupling network adjacency matrix describes the coupling relationship between the nodes in the distribution network and the nodes in the communication network. Through this coupling network adjacency matrix, we can more clearly understand the interaction and influence between the distribution network and the communication network.

[0111] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0112] The present application also provides a dispatching device for a distribution network. It should be noted that a dispatching device for a distribution network in an embodiment of the present application can be used to execute a dispatching method for a distribution network provided in an embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0113] The following introduces a dispatching device for a distribution network provided in an embodiment of the present application.

[0114] Figure 3 1 is a structural block diagram of a dispatching device for a distribution network according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0115] A first establishing unit 10 is used to construct a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between the nodes in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between the nodes in the distribution network;

[0116] A first division unit 20 is used for dividing the distribution network into a plurality of distribution network communities according to the distribution network weight matrix using a Louvain community discovery algorithm to obtain a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two of the nodes in the distribution network community is greater than a predetermined threshold, and the first community structure is a structural domain composed of the distribution network communities in the distribution network, and the electrical distance is a change in voltage amplitude or phase angle between the nodes;

[0117] A first constraint unit 30, used for establishing constraint conditions to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint conditions;

[0118] A second establishing unit 40 is used to construct a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, wherein the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network;

[0119] A third establishing unit 50 is used to construct a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, wherein the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network;

[0120] The first control unit 60 is used to perform power dispatching on the distribution network according to the coupling network adjacency matrix so as to balance the power supply and demand of the distribution network.

[0121] Through this embodiment, the first establishing unit is used to construct a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, the above-mentioned distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the above-mentioned distribution network, and the above-mentioned distribution network weight matrix is ​​used to describe the electrical coupling strength between each of the above-mentioned nodes in the above-mentioned distribution network; the first dividing unit is used to divide the above-mentioned distribution network into multiple distribution network communities according to the above-mentioned distribution network weight matrix using the Louvain community discovery algorithm to obtain a first community structure, the above-mentioned distribution network community is a part of the above-mentioned distribution network, and the electrical distance between any two of the above-mentioned nodes in the above-mentioned distribution network community is greater than a predetermined threshold value, and the above-mentioned first community structure is a structural domain composed of each of the above-mentioned distribution network communities in the above-mentioned distribution network, and the above-mentioned electrical distance is the voltage amplitude between each of the above-mentioned nodes. The change of value or phase angle; the first constraint unit is used to establish constraint conditions to optimize the above-mentioned first community structure to obtain the second community structure, so that the above-mentioned second community structure satisfies the above-mentioned constraint conditions; the second establishing unit is used to construct the communication network adjacency matrix of the above-mentioned communication network according to the hierarchical structure of the communication network, and the above-mentioned communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network; the third establishing unit is used to construct a coupling network adjacency matrix according to the above-mentioned distribution network adjacency matrix, the above-mentioned second community structure, and the above-mentioned communication network adjacency matrix, and the above-mentioned coupling network adjacency matrix is ​​used to describe the coupling relationship between the above-mentioned distribution network and the above-mentioned communication network; the first control unit is used to perform power dispatch on the above-mentioned distribution network according to the above-mentioned coupling network adjacency matrix, so as to balance the power supply and demand of the above-mentioned distribution network. The present application obtains the electrical coupling strength of each node in the distribution network by constructing a distribution network adjacency matrix and a distribution network weight matrix, divides each node in the distribution network into communities according to the Louvain community discovery algorithm, and constructs a communication network adjacency matrix according to the hierarchical structure of the communication network. The coupling relationship between the distribution network and the communication network is established through the distribution network adjacency matrix and the communication network adjacency matrix. According to the coupling relationship, dispatchers can make better decisions on the power dispatching of the distribution network, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art.

[0122] In order to obtain the distribution network adjacency matrix and the distribution network weight matrix, in an optional implementation, the distribution network adjacency matrix and the distribution network weight matrix are constructed according to the topological structure of the distribution network. The first establishing unit includes:

[0123] The first establishment subunit is used to construct the distribution network undirected graph G of the distribution network according to the topological structure of the distribution network. p =(V p ,E p ,W p ), where V p is the set of nodes in the above distribution network, E pis the set of edges in the above distribution network, W p is a set consisting of the weights of the edges in the distribution network, the nodes are the power equipment in the distribution network, the edges are the lines connecting the nodes in the distribution network, and the weights of the edges are the electrical coupling strengths between the nodes;

[0124] Specifically, an undirected graph is constructed according to the topological structure of the distribution network, in which the nodes represent the power equipment in the distribution network, the edges represent the lines connecting these devices, and the edge weights represent the strength of the electrical coupling between the devices. This graph representation helps to intuitively display the relationship and connection between the devices in the distribution network, facilitating network analysis and optimization.

[0125] The second establishment subunit is used to determine the connection relationship between the above nodes according to the above topological structure of the above distribution network, and obtain the above distribution network adjacency matrix Among them, a ij For node v i With node v j The connection relationship between them, n is the total number of nodes in the above distribution network;

[0126] Specifically, the connection between nodes in the distribution network is described by the distribution network adjacency matrix, where each element represents the connection relationship between nodes. i With node v j If there is a connection between them, the value of the element of the corresponding distribution network adjacency matrix is ​​1; if there is no connection, the value is 0. The distribution network adjacency matrix can clearly show the connection relationship between the nodes in the distribution network.

[0127] The third subunit is used to calculate the above distribution network weight matrix according to the electrical distance. Among them, w ij For node v i With node v j The weights of the above edges are determined by the weight matrix of the distribution network, and the above distribution network weight matrix is ​​used to describe the electrical coupling strength between the above nodes.

[0128] Specifically, the distribution network weight matrix is ​​calculated based on the electrical distance, which describes the electrical coupling strength between nodes. Each element represents the weight of the edge between nodes, that is, a measure of the electrical coupling strength. This matrix helps to analyze the degree of association and influence between nodes in the distribution network. The effect of this expression is that the electrical coupling strength between nodes in the distribution network can be clearly described through the weight matrix.

[0129] In order to determine the distribution network weight matrix through the sensitivity matrix, in an optional implementation, the distribution network weight matrix is ​​obtained according to the electrical distance calculation, and the third establishment module includes:

[0130] The first establishment module is used to use active sensitivity and reactive sensitivity to represent the above electrical distance, wherein the above active sensitivity is the influence of the active power change of the node on the voltage amplitude or phase angle change of other nodes, and the above reactive sensitivity is the influence of the reactive power change of the node on the voltage amplitude or voltage phase angle change of other nodes;

[0131] Specifically, active sensitivity and reactive sensitivity are used to represent the electrical distance, where active sensitivity represents the impact of changes in active power of a node on changes in voltage amplitude or phase angle of other nodes, and reactive sensitivity represents the impact of changes in reactive power of a node on changes in voltage amplitude or phase angle of other nodes. By representing active and reactive sensitivities, the coupling relationship and electrical distance between nodes can be more accurately described, helping to understand and optimize the electrical characteristics between different nodes in the distribution network.

[0132] The second building module is used to build a sensitivity matrix according to the above active sensitivity and the above reactive sensitivity. Among them, S Pδ is the phase angle active matrix, which is used to describe the impact of the change in active power ΔP of each of the above nodes on the phase angle Δδ of the corresponding node, S PV is the voltage amplitude active matrix, which is used to describe the influence of the change in active power ΔP of each of the above nodes on the voltage amplitude ΔV of the corresponding node, S Qδ is the phase angle reactive matrix, which is used to describe the influence of the change of reactive power ΔP of each node on the phase angle Δδ of the corresponding node. QV is a voltage amplitude reactive matrix, which is used to describe the influence of the change in reactive power ΔP of each of the above nodes on the voltage amplitude ΔV of the corresponding node. The above sensitivity matrix is ​​used to describe the influence relationship between the power change and the voltage change of each of the above nodes in the above distribution network.

[0133] Specifically, a sensitivity matrix is ​​constructed based on active and reactive sensitivities, which includes phase angle active matrix, voltage amplitude active matrix, phase angle reactive matrix and voltage amplitude reactive matrix. These matrices describe the influence of node power changes on the corresponding node phase angle and voltage amplitude. Through the sensitivity matrix, the correlation between node power changes and voltage changes can be quantified, and the mutual influence and coupling degree between nodes in the power system can be understood.

[0134] The third establishing module is used to calculate the distribution network weight matrix according to the sensitivity matrix.

[0135] Specifically, the sensitivity matrix is ​​used to calculate the distribution network weight matrix representing the electrical coupling strength between nodes in the distribution network. By calculating the sensitivity matrix, the impact of node power changes on voltage changes can be quantified, thereby determining the coupling strength between nodes and forming a weight matrix.

[0136] In order to determine each element in the distribution network weight matrix, in an optional implementation, the distribution network weight matrix is ​​calculated according to the sensitivity matrix, and the third establishment module includes:

[0137] The first submodule is used to calculate the reactive electrical distance between the nodes in the distribution network according to the sensitivity matrix. in, When node v j When the reactive power changes, the node v j With node v i Ratio of voltage amplitude change, S QV,ij is the element in the i-th row and j-th column of the voltage amplitude reactive matrix above, representing the node v j Reactive power change to node v i The impact of voltage amplitude change, ΔV Qj For node v j The impact of reactive power changes on voltage amplitude changes;

[0138] Specifically, the reactive electrical distance between nodes in the distribution network is calculated using the sensitivity matrix, which takes into account the impact of reactive power changes on voltage amplitude changes. By calculating the reactive electrical distance, the impact of reactive power changes between nodes on voltage amplitude changes can be evaluated, and the correlation between reactive power changes and voltage amplitude changes between nodes can be quantified, which helps to more accurately evaluate and manage the mutual impact of reactive power between nodes in the distribution network.

[0139] The second submodule is used to calculate the active electrical distance between the nodes in the distribution network according to the sensitivity matrix. in, When node v j When the active power changes, the node v j With node v i Ratio of voltage amplitude change, S PV,ij is the element in the i-th row and j-th column of the voltage amplitude active matrix above, representing the node v j Active power change to node v i The impact of voltage amplitude change, ΔV Pj For node v jThe impact of active power changes on voltage amplitude changes;

[0140] Specifically, the sensitivity matrix is ​​used to calculate the active electrical distance between nodes in the distribution network, which takes into account the impact of active power changes on voltage amplitude changes. By calculating the active electrical distance, the impact of active power changes between nodes on voltage amplitude changes can be evaluated, and the correlation between active power changes and voltage amplitude changes between nodes can be quantified, which helps to more accurately evaluate and manage the mutual influence of active power between nodes in the distribution network.

[0141] The third submodule is used to obtain the electrical distance between each of the nodes according to the reactive electrical distance and the active electrical distance:

[0142] Specifically, the electrical distance between nodes is calculated by comprehensively considering the reactive electrical distance and the active electrical distance. This method integrates the impact of node power changes on voltage changes and quantifies the electrical coupling relationship between nodes.

[0143] The fourth submodule is used to calculate the elements w in the distribution network weight matrix according to the electrical distance. ij =(1-L ij ) / max(L).

[0144] Specifically, the value of each element in the weight matrix is ​​obtained by calculating the electrical distance. According to the calculation results of the electrical distance, the coupling degree between the nodes in the weight matrix is ​​determined, providing quantitative information about the interaction between the nodes.

[0145] In order to obtain the first community structure, in an optional implementation, the Louvain community discovery algorithm is used to divide the distribution network into multiple distribution network communities according to the distribution network weight matrix to obtain the first community structure. The first division unit includes:

[0146] A first partitioning module is used to treat each node in the distribution network as a community and obtain the current community of each node;

[0147] Specifically, each node in the distribution network is regarded as a community, and the current community of each node is obtained. This approach regards each node as a whole, emphasizes the integrity of the node, and provides a basis for the subsequent establishment of the community.

[0148] The second partitioning module is used to execute the first allocation step, to allocate any target node among the above nodes to the above current community corresponding to other nodes having a connection relationship with the above target node, and to calculate the modularity gain of each of the above current communities. Among them, Σ inis the sum of the weights of all the above edges in the current community, Σ tot is the sum of the weights of the edges connecting the nodes in the current community and other communities, k i For node v i The sum of the weights of the above edges of the connected nodes, k i,in For node v i The sum of the weights of the above edges within the current community, m is the sum of the weights of all the above edges in the above distribution network;

[0149] Specifically, in the first allocation step, each target node is allocated to other nodes in the current community that have a connection relationship with it, and then the modularity gain of the current community is calculated. The modularity gain is used to measure the connection strength within the community and the connection strength between communities. The calculation of modularity gain provides a quantitative indicator for community optimization, which helps guide community allocation and structural adjustment.

[0150] A third division module is used to execute the first determination step, and is used to determine the current community with the largest modularity gain according to the modularity gains of the current communities, and determine it as the maximum gain community;

[0151] Specifically, in the first determination step, the modularity gains of various current communities are compared, and the current community with the maximum modularity gain is determined and determined as the maximum gain community. Determining the maximum gain community helps to identify the most optimized part of the community structure, making the internal connection of the community closer.

[0152] A fourth partitioning module is used to execute the second allocation step, and when the modularity gain of the maximum gain community is greater than 0, allocate the target node to the maximum gain community, and update the nodes included in the current maximum gain community;

[0153] Specifically, in the second allocation step, if the modularity gain of the maximum gain community is greater than 0, the target node is allocated to the maximum gain community, and the nodes included in the current maximum gain community are updated, thereby optimizing the community structure and improving the compactness and efficiency of the community.

[0154] A fifth partitioning module, configured to execute the third allocation step, wherein when the modularity gain of the community with the largest modularity gain is less than 0, the target node is not allocated;

[0155] Specifically, in the third allocation step, if the modularity gain of the community with the largest modularity gain is less than 0, the target node will not be allocated. The purpose of this step is to consider that the structure of the community is not ideal or optimized when the modularity gain of the community with the largest modularity gain is negative, and therefore choose not to allocate the target node.

[0156] The sixth partitioning module is used to execute the first repetition step, repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence, until each of the above nodes is no longer allocated, and then each of the above current communities is determined as a super node, and a target network structure is formed according to the super nodes;

[0157] Specifically, in the first repetitive step, the first allocation step, the first determination step, the second allocation step and the third allocation step are executed cyclically at least once until all nodes are no longer allocated. At this time, each current community is determined to be a super node. This iterative process continuously optimizes the community allocation and eventually forms a new network structure, in which the super node represents an optimized set of nodes. Through iterative cycles, the community structure is continuously optimized and adjusted to improve the correlation between nodes.

[0158] The seventh division module is used to execute the second repetitive step, repeating the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence, until the modularity gain of each of the current communities in the target network structure no longer changes, thereby obtaining a first community structure.

[0159] Specifically, in the second repetitive step, the first allocation step, the first determination step, the second allocation step and the third allocation step are repeated in sequence until the modularity gain of each current community in the target network structure no longer changes, thereby forming a first community structure. Through multiple iterative repetitive steps, the community structure can be effectively optimized to make it more adaptable to and optimize the needs of the distribution network, thereby improving the reliability of the system.

[0160] In order to obtain the second community structure, in an optional implementation, a constraint condition is established to optimize the first community structure to obtain the second community structure, and the first constraint unit includes:

[0161] A first constraint module, used for executing a first constraint condition, used for setting each of the above communities in the above first community structure to include at least one power node and one load node;

[0162] Specifically, the first constraint condition stipulates that each community in the first community structure contains at least one power node and one load node. This constraint condition ensures that there are power nodes and load nodes in each community to maintain the power supply and load balance of the distribution network and ensure the normal operation and stability of the power system.

[0163] The second constraint module is used to execute the second constraint condition and to set the number of the distribution network communities in the first community structure max(N min ,N f -2)≤n≤min(N max ,Nf ), where Nmin is the minimum number threshold of the distribution network community in the first community structure, N max is the maximum number threshold of the distribution network community in the first community structure, N f is the cube root of the total number of the above nodes in the above distribution network;

[0164] Specifically, the second constraint stipulates the number of distribution network communities in the first community structure. Such a constraint helps to control the number of communities and ensure the rationality and effectiveness of the community structure. Too many communities may make the model too complex and increase the computational burden; too few communities may make the model too rough and unable to accurately reflect the local characteristics of the network.

[0165] A third constraint module, used for executing a third constraint condition, used for setting the ratio of the total power of the load nodes in each of the communities in the first community structure to the total power of the power supply nodes to be between 0.6 and 1.5;

[0166] Specifically, the third constraint stipulates that the ratio of the total power of the load nodes to the total power of the power supply nodes of each community in the first community structure should be between 0.6 and 1.5. This constraint helps to maintain the supply and demand balance within each community and ensure a reasonable match between load and power supply, thereby ensuring the stable operation of the distribution network and the balance of power supply load.

[0167] The fourth constraint module is used to optimize the first community structure according to the first constraint condition, the second constraint condition and the third constraint condition to obtain a second community structure.

[0168] Specifically, by optimizing the community structure that meets the first, second and third constraints, an adjusted and improved second community structure is obtained, which makes the second community structure more reasonable and optimized while meeting the constraints. The optimized second community structure helps to maintain the balance between supply and demand and the reasonable connection relationship between communities.

[0169] In order to obtain the communication network adjacency matrix, in an optional implementation manner, the communication network adjacency matrix of the communication network is constructed according to the hierarchical structure of the communication network, and the second establishing unit includes:

[0170] The fourth sub-unit is used to divide the communication network into an access layer, a backbone layer and a core layer according to the hierarchical structure of the communication network, wherein the access layer is used to collect information of the distribution network and transmit dispatch control signals, the backbone layer is used for interconnection between communities and forwarding information from the access layer, the core layer is interconnected with the regional control center in the backbone layer, including a main dispatch center and a backup dispatch center, and the core layer is used for information processing;

[0171] Specifically, according to the hierarchical structure of the communication network, the communication network can be divided into an access layer, a backbone layer and a core layer. The access layer is mainly responsible for collecting distribution network information and transmitting control signals. The backbone layer is mainly responsible for the connection between communities and information forwarding. The core layer connects the regional control center, including the main dispatching center and the backup dispatching center, and is responsible for information processing. This hierarchical design ensures the transmission of information flow and realizes efficient collaboration between the physical and information levels in the smart grid.

[0172] The fifth establishing subunit is used to construct a communication network undirected graph G according to the network structure of the above communication network. c =(V c ,E c ), where V c is a collection of nodes in the above communication network, including the information nodes in the above access layer, the regional control nodes in the above backbone layer, the dispatch center nodes in the above core layer, c is the set of the above edges in the above communication network;

[0173] Specifically, based on the hierarchical architecture of the communication network, an undirected graph model is constructed, whose node set covers the information nodes of the access layer, the regional control nodes of the backbone layer, and the scheduling center nodes of the core layer, and the edge set defines the interconnection relationship between the nodes.

[0174] A sixth establishing subunit, used for determining the number of the information nodes and the number of the regional control nodes in the communication network, the number of the information nodes in the communication network being equal to the number of nodes in the distribution network, and the number of the regional control nodes being equal to the number of distribution network communities in the second community structure in the distribution network;

[0175] Specifically, it ensures that the number of information nodes in the communication network corresponds one-to-one with the nodes in the distribution network. At the same time, the number of deployed regional control nodes also accurately matches the number of communities in the distribution network.

[0176] A seventh establishment subunit is used to add the above-mentioned information nodes in the above-mentioned communication network based on the turntable idea to form a final communication network community, wherein the above-mentioned final communication network community is an area formed by the above-mentioned information nodes and the connection relationship between each of the above-mentioned information nodes, and the above-mentioned information nodes correspond one-to-one to each of the above-mentioned nodes in the above-mentioned distribution network;

[0177] Specifically, using the random selection mechanism of the turntable, we integrate information nodes into the communication network to form a final communication community structure that is directly mapped to the distribution network nodes. Through this node addition strategy and community construction method, we achieve precise docking between the communication network and the distribution network.

[0178] The eighth establishing subunit is used to obtain the above communication network adjacency matrix according to the above final communication network community. Wherein, m is the total number of the above information nodes.

[0179] Specifically, according to the final communication network community we constructed, we determine the communication network adjacency matrix. Through the communication network adjacency matrix, we can determine the association relationship between each information node and establish a good connection for each information node.

[0180] In order to obtain the final communication network community, in an optional implementation manner, based on the turntable concept, the information node is added to the communication network to form the final communication network community, and the seventh establishing subunit includes:

[0181] The fourth building module is used to construct the community priority selection probability function Among them, C i is the number of nodes in the ith communication network community, and the above communication network community corresponds one-to-one to the above distribution network community;

[0182] Specifically, through the community priority selection probability function, the communication network community can be given priority. The calculation method of this probability function is determined according to the number of nodes in each communication network community. Each communication network community corresponds to a distribution network community. When selecting a community, the number of nodes in the communication network community is given priority to ensure that the communication needs can be better met when building the network.

[0183] The fifth building module is used to construct a node priority connection probability function Among them, δ Ai is the probability of node i being selected in the Ath communication network community, k Ai is the number of the above edges of node i in the Ath communication network community, k max is the maximum number of the above edges of the above nodes in the communication network community;

[0184] Specifically, constructing a node priority connection probability function can make the priority of node connection more reasonable and efficient. By considering the number of edges of the node and the maximum number of node edges in the entire community, the importance of the node and the closeness of the connection can be better measured, thereby achieving a more effective node connection strategy.

[0185] The sixth establishing module is used to construct the above-mentioned final communication network community according to the above-mentioned community priority selection probability function and the above-mentioned node priority connection probability function.

[0186] Specifically, through the community priority selection probability function and the node priority connection probability function, we constructed a final communication network community. The two functions can effectively organize nodes into communities, thereby improving the efficiency and performance of the network and forming a closer community structure.

[0187] In order to obtain the coupling network adjacency matrix, in an optional implementation, the coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, and the third establishing unit includes:

[0188] The ninth establishment subunit is used to construct the coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. Among them, A p-c It is an association matrix, which is used to describe the coupling relationship between the nodes in the distribution network and the nodes in the communication network.

[0189] Specifically, a coupling network adjacency matrix was constructed based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. This coupling network adjacency matrix describes the coupling relationship between the nodes in the distribution network and the nodes in the communication network. Through this coupling network adjacency matrix, we can more clearly understand the interaction and influence between the distribution network and the communication network.

[0190] The above-mentioned dispatching device of a distribution network includes a processor and a memory. The above-mentioned first establishment unit, the first division unit, the first constraint unit, the second establishment unit, the third establishment unit, the first control unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are respectively located in different processors in the form of any combination. The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the power dispatching efficiency of the distribution network can be improved by adjusting the kernel parameters. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0191] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0192] 1) A distribution network dispatching method of the present application obtains the electrical coupling strength of each node in the distribution network by constructing a distribution network adjacency matrix and a distribution network weight matrix, divides each node in the distribution network into communities according to the Louvain community discovery algorithm, and constructs a communication network adjacency matrix according to the hierarchical structure of the communication network. The coupling relationship between the distribution network and the communication network is established through the distribution network adjacency matrix and the communication network adjacency matrix. According to the coupling relationship, the dispatcher can make better decisions on the power dispatching of the distribution network, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art.

[0193] 2) A distribution network dispatching device of the present application obtains the electrical coupling strength of each node in the distribution network by constructing a distribution network adjacency matrix and a distribution network weight matrix, divides each node in the distribution network into communities according to the Louvain community discovery algorithm, and constructs a communication network adjacency matrix according to the hierarchical structure of the communication network. A coupling relationship between the distribution network and the communication network is established through the distribution network adjacency matrix and the communication network adjacency matrix. According to the coupling relationship, dispatchers can make better decisions on the power dispatching of the distribution network, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the distribution network and the communication network in the prior art.

[0194] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dispatching a distribution network, characterized in that: include: Constructing a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between each node in the distribution network; The distribution network is divided into a plurality of distribution network communities according to the distribution network weight matrix by using a Louvain community discovery algorithm to obtain a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two nodes in the distribution network community is greater than a predetermined threshold, the first community structure is a structural domain composed of the distribution network communities in the distribution network, and the electrical distance is a change in the voltage amplitude or phase angle between the nodes; Establishing constraint conditions to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint conditions; Constructing a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, wherein the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network; Constructing a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, wherein the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network; The power distribution network is dispatched according to the coupling network adjacency matrix to balance the power supply and demand of the distribution network.

2. The method according to claim 1, characterized in that According to the topological structure of the distribution network, the distribution network adjacency matrix and the distribution network weight matrix are constructed, including: According to the topological structure of the distribution network, an undirected distribution network graph G of the distribution network is constructed. p =(V p ,E p ,W p ), where V p is the set of nodes in the distribution network, E p is the set of edges in the distribution network, W p is a set consisting of the weights of the edges in the distribution network, the nodes are power equipment in the distribution network, the edges are lines connecting the nodes in the distribution network, and the weights of the edges are the electrical coupling strengths between the nodes; Determine the connection relationship between the nodes according to the topological structure of the distribution network to obtain the adjacency matrix of the distribution network Among them, a ij For node v i With node v j The connection relationship between them, n is the total number of nodes in the distribution network; The distribution network weight matrix is ​​obtained according to the electrical distance calculation Among them, w ij For node v i With node v j The weight matrix is ​​used to describe the electrical coupling strength between the nodes.

3. The method according to claim 2, characterized in that The distribution network weight matrix is ​​obtained according to the electrical distance calculation, including: The electrical distance is represented by active sensitivity and reactive sensitivity, wherein the active sensitivity is the influence of the active power change of a node on the voltage amplitude or phase angle change of other nodes, and the reactive sensitivity is the influence of the reactive power change of a node on the voltage amplitude or voltage phase angle change of other nodes; A sensitivity matrix is ​​constructed according to the active sensitivity and the reactive sensitivity. Among them, S Pδ is the phase angle active power matrix, which is used to describe the influence of the change of active power ΔP of each node on the phase angle Δδ of the corresponding node, S PV is the voltage amplitude active matrix, which is used to describe the influence of the change of active power ΔP of each node on the voltage amplitude ΔV of the corresponding node, S Qδ is the phase angle reactive matrix, which is used to describe the influence of the change of reactive power ΔP of each node on the phase angle Δδ of the corresponding node, S QV is a voltage amplitude reactive matrix, which is used to describe the influence of the change in reactive power ΔP of each node on the voltage amplitude ΔV of the corresponding node, and the sensitivity matrix is ​​used to describe the influence relationship between the power change and the voltage change of each node in the distribution network; The distribution network weight matrix is ​​calculated according to the sensitivity matrix.

4. The method according to claim 3, characterized in that The distribution network weight matrix is ​​calculated according to the sensitivity matrix, including: The reactive electrical distance between the nodes in the distribution network is obtained by calculating the sensitivity matrix in, When node v j When the reactive power changes, the node v j With node v i Ratio of voltage amplitude change, S QV,ij is the element in the i-th row and j-th column of the voltage amplitude reactive matrix, representing the node v j Reactive power change to node v i The impact of voltage amplitude change, ΔV Qj For node v j The impact of reactive power changes on voltage amplitude changes; The active electrical distance between the nodes in the distribution network is calculated according to the sensitivity matrix in, When node v j When the active power changes, the node v j With node v i Ratio of voltage amplitude change, S PV,ij is the element in the i-th row and j-th column of the voltage amplitude active matrix, representing the node v j Active power change to node v i The impact of voltage amplitude change, ΔV Pj For node v j The impact of active power changes on voltage amplitude changes; The electrical distance between the nodes is obtained according to the reactive electrical distance and the active electrical distance: Each element in the distribution network weight matrix is ​​obtained by calculating the electrical distance w ij =(1-L ij ) / max(L)。 5. The method according to claim 2, characterized in that: The distribution network is divided into a plurality of distribution network communities according to the weight matrix using the Louvain community discovery algorithm to obtain a first community structure, including: Taking each node in the distribution network as a community, obtaining the current community of each node; The first allocation step is to allocate any target node among the nodes to the current community corresponding to other nodes having a connection relationship with the target node, and calculate the modularity gain of each current community. Among them, Σ in is the sum of the weights of all the edges in the current community, Σ tot is the sum of the weights of the edges connecting the nodes in the current community and other communities, k i For node v i The sum of the weights of the edges connecting the nodes, k i,in For node v i The sum of the weights of the edges within the current community, m is the sum of the weights of all the edges in the distribution network; A first determination step is used to determine the current community with the largest modularity gain according to the modularity gains of the current communities, and determine it as the maximum gain community; A second allocation step, when the modularity gain of the maximum gain community is greater than 0, the target node is allocated to the maximum gain community, and the nodes included in the current maximum gain community are updated; In a third allocation step, when the modularity gain of the community with the largest modularity gain is less than 0, the target node is not allocated; A first repetition step, repeating the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence, until each of the nodes is no longer allocated, and determining each of the current communities as a super node at this time, and forming a target network structure according to the super nodes; The second repetition step is to repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once in sequence until the modularity gain of each of the current communities in the target network structure no longer changes, thereby obtaining a first community structure.

6. The method according to claim 1, characterized in that Establishing constraint conditions to optimize the first community structure to obtain a second community structure, including: A first constraint condition is used to set each of the communities in the first community structure to include at least one power node and one load node; The second constraint is used to set the number of the distribution network communities in the first community structure max(N min ,N f -2)≤n≤min(N max ,N f ), where Nmin is the minimum number threshold of the distribution network community in the first community structure, N max is the maximum number threshold of the distribution network community in the first community structure, N f is the cube root of the total number of the nodes in the distribution network; A third constraint condition is used to set the ratio of the total power of the load nodes to the total power of the power supply nodes in each of the communities in the first community structure to be between 0.6 and 1.5; The first community structure is optimized according to the first constraint condition, the second constraint condition and the third constraint condition to obtain a second community structure.

7. The method according to claim 2, characterized in that Constructing a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, including: According to the hierarchical structure of the communication network, the communication network is divided into an access layer, a backbone layer and a core layer. The access layer is used to collect information of the distribution network and transmit dispatch control signals. The backbone layer is used for interconnection between communities and forwarding information from the access layer. The core layer is interconnected with the regional control center in the backbone layer, including a main dispatch center and a standby dispatch center. The core layer is used for information processing. Construct a communication network undirected graph G according to the network structure of the communication network c =(V c ,E c ), where V c is a collection of nodes in the communication network, including information nodes in the access layer, regional control nodes in the backbone layer, and dispatch center nodes in the core layer. c is a set of edges in the communication network; Determine the number of the information nodes and the number of the regional control nodes in the communication network, the number of the information nodes in the communication network is equal to the number of nodes in the distribution network, and the number of the regional control nodes is equal to the number of the distribution network communities in the second community structure in the distribution network; Based on the turntable concept, the information nodes are added to the communication network to form a final communication network community, wherein the final communication network community is an area formed by the information nodes and the connection relationship between the information nodes, and the information nodes correspond one to one to the nodes in the distribution network; The communication network adjacency matrix is ​​obtained according to the final communication network community Wherein, m is the total number of the information nodes.

8. The method according to claim 7, characterized in that Based on the turntable concept, the information nodes are added to the communication network to form a final communication network community, including: Constructing community preference probability function Among them, C i is the number of nodes in the ith communication network community, and the communication network community corresponds one-to-one to the distribution network community; Constructing node priority connection probability function Among them, δ Ai is the probability of node i being selected in the Ath communication network community, k Ai is the number of edges of node i in the Ath communication network community, k max is the maximum value of the number of edges of the node in the communication network community; The final communication network community is constructed according to the community priority selection probability function and the node priority connection probability function.

9. The method according to claim 1, characterized in that: Constructing a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, including: The coupling network adjacency matrix is ​​constructed according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. Among them, A p-c It is an association matrix, which is used to describe the coupling relationship between the nodes in the distribution network and the nodes in the communication network.

10. A dispatching device for a distribution network, characterized in that: include: A first establishing unit is used to construct a distribution network adjacency matrix and a distribution network weight matrix according to the topological structure of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationship between each node in the distribution network, and the distribution network weight matrix is ​​used to describe the electrical coupling strength between each node in the distribution network; a first division unit, configured to divide the distribution network into a plurality of distribution network communities according to the distribution network weight matrix by adopting a Louvain community discovery algorithm, and obtain a first community structure, wherein the distribution network community is a part of the distribution network, and the electrical distance between any two nodes in the distribution network community is greater than a predetermined threshold, and the first community structure is a structural domain composed of the distribution network communities in the distribution network, and the electrical distance is a change in voltage amplitude or phase angle between the nodes; A first constraint unit, configured to establish constraint conditions to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint conditions; A second establishing unit is used to construct a communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network, wherein the communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network; a third establishing unit, configured to establish a coupling network adjacency matrix according to the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, wherein the coupling network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network; The first control unit is used to perform power dispatching on the distribution network according to the coupling network adjacency matrix so as to balance the power supply and demand of the distribution network.

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