A power distribution network dispatching method and a dispatching device

Abstract

The application provides a power distribution network scheduling method and a scheduling device. The method comprises the following steps: constructing a power distribution network adjacency matrix and a power distribution network weight matrix according to a topology 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 using a Louvain community finding 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 adjacency matrix of a communication network according to a 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, the coupling network adjacency matrix being used to describe a coupling relationship between the power distribution network and the communication network; and performing power scheduling on the power distribution network according to the coupling network adjacency matrix to balance power supply and demand of the power distribution network. The method solves the problem of low power scheduling efficiency caused by a low coupling relationship between the power distribution network and the communication network in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power grid dispatching, in particular to a power distribution network dispatching method and device. BACKGROUND

[0002] With the continuous development of smart grid, the intelligent, information-based and automated level of power system is continuously improved, and a large number of advanced computers, communication and control technologies are applied to the power grid. The number of information-based devices such as sensors, actuators and communication equipment is rapidly growing, making the connection between information networks and power networks more complex. Modern power systems have developed into complex multi-dimensional heterogeneous power information-physical systems that are coupled with information systems (power communication networks) and physical systems (power grids). Among them, the traditional power distribution network and the communication network are deeply integrated to form a power distribution network information-physical system.

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

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

[0005] To achieve the above objectives, according to one aspect of this application, a method for scheduling a distribution network is provided, comprising: constructing a distribution network adjacency matrix and a distribution network weight matrix based on the topology of the distribution network, wherein the distribution network adjacency matrix is ​​used to describe the connection relationships between 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; using the Louvain community discovery algorithm to divide the distribution network into multiple distribution network communities based on the distribution network weight matrix 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, and the first community structure comprises each distribution network community in the distribution network. The structure is composed of a structural domain, where 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 that satisfies the constraints; a communication network adjacency matrix is ​​constructed based on the hierarchical structure of the communication network, which describes the connection relationship between information nodes in the communication network; a coupled network adjacency matrix is ​​constructed based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, which describes the coupling relationship between the distribution network and the communication network; power dispatch is performed on the distribution network based on the coupled network adjacency matrix to achieve a balance between power supply and demand in the distribution network.

[0006] Optionally, constructing a distribution network adjacency matrix and a distribution network weight matrix based on the distribution network topology includes: constructing an undirected graph G of the distribution network based on the distribution network topology. p =(V p E p W p ), where V p E is the set of all nodes in the power distribution network. p Let W be the set of all edges in the power distribution network. p The set of weights for each edge in the distribution network, where each node is a power device in the distribution network, each edge is a line connecting each node in the distribution network, and the weight of each edge represents the electrical coupling strength between each node; the connection relationships between each node are determined based on the topology of the distribution network to obtain the distribution network adjacency matrix. Among them, a ij For node v i With node v j The connection relationships between them, where n is the total number of nodes in the distribution network; the weight matrix of the distribution network is calculated based on the electrical distance. Among them, w ij For node vi the weight of the edge between node v j The power grid weight matrix is used to describe the electrical coupling strength between the nodes.

[0007] Optionally, the power grid weight matrix is calculated according to the electrical distance, comprising: representing the electrical distance by using active sensitivity and reactive sensitivity, 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 phase angle change of other nodes; constructing a sensitivity matrix S according to the active sensitivity and the reactive sensitivity wherein S Pδ is a phase angle active matrix, used to describe the influence of change amount ΔP of active power of each node on corresponding node phase angle Δδ, S PV is a voltage amplitude active matrix, used to describe the influence of change amount ΔP of active power of each node on corresponding node voltage amplitude ΔV, S Qδ is a phase angle reactive matrix, used to describe the influence of change amount ΔP of reactive power of each node on corresponding node phase angle Δδ, S QV is a voltage amplitude reactive matrix, used to describe the influence of change amount ΔP of reactive power of each node on corresponding node voltage amplitude ΔV, and the sensitivity matrix is used to describe the influence relationship between power change amount and voltage change amount of each node in the power grid; and the power grid weight matrix is calculated according to the sensitivity matrix.

[0008] Optionally, the power grid weight matrix is calculated according to the sensitivity matrix, comprising: calculating the reactive electrical distance between each node in the power grid according to the sensitivity matrix wherein, i=1, 2…n, j=1, 2…n, is the ratio of change amount of reactive power of node v j to change amount of voltage amplitude of node v j is the ratio of change amount of active power of node v i to change amount of voltage amplitude of node v QV,ij is an element in the i-th row and the j-th column of the voltage amplitude reactive matrix, representing the influence of change amount of reactive power of node v j on change amount of voltage amplitude of node v i is an element in the i-th row and the j-th column of the voltage amplitude active matrix, representing the influence of change amount of active power of node v Qj on change amount of voltage amplitude of node v j is the influence of change amount of reactive power of node v wherein, is the ratio of change amount of active power of node vj Active power change of node v j With node v i The ratio of the voltage amplitude change amount, S PV,ij is the element in the i-th row and j-th column of the voltage amplitude active power matrix, indicating the active power change of node v j The active power change amount of node v i The impact of the voltage amplitude change amount, ΔV Pj is the element in the i-th row and j-th column of the voltage amplitude active power matrix, indicating the active power change of node v j The impact of the active power change amount on the voltage amplitude change amount; the electrical distance between each node is obtained according to the reactive electrical distance and the active electrical distance Each element w in the power distribution network weight matrix is calculated according to the electrical distance ij =(1-L ij ) / max(L).

[0009] Optionally, the Louvain community discovery algorithm is used to divide the power distribution network into multiple power distribution network communities according to the power distribution network weight matrix, to obtain a first community structure, including: taking each node in the power distribution network as a community to obtain the current community of each node; a first distribution step is used to distribute any target node in each node to the current community corresponding to the other nodes having a connection relationship with the target node, and to calculate the modularity gain of each current community wherein Σ in is the sum of the weights of all 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 is the sum of the weights of the edges connecting the nodes with node v i is the sum of the weights of the edges connecting the nodes with node v i,in is the sum of the weights of the edges connecting the nodes with node v iThe sum of the weights of the edges within the current community, where m is the sum of the weights of all the edges in the distribution network; a first determining step, used to determine the current community with the largest modularity gain based on the modularity gain of each current community, and determine it as the maximum gain community; a second allocation step, if the modularity gain of the maximum gain community is greater than 0, to allocate the target node to the maximum gain community, and update the nodes included in the current maximum gain community; a third allocation step, if the modularity gain of the community with the largest modularity gain is less than 0, the target node is not allocated; a first repeating step, repeating the first allocation step, the first determining step, the second allocation step, and the third allocation step at least once in sequence until each node is no longer allocated, and determining each current community at this time as a super node, and forming a target network structure based on the super nodes; a second repeating step, repeating the first allocation step, the first determining step, the second allocation step, and the third allocation step at least once in sequence until the modularity gain of each current community in the target network structure no longer changes, and obtaining the first community structure.

[0010] Optionally, constraints are established to optimize the first community structure to obtain a second community structure, including: a first constraint setting that each community in the first community structure includes at least one power supply node and one load node; and a second constraint setting the maximum number (N) of distribution network communities in the first community structure. min N f -2)≤n≤min(N max N f ), where Nmin is the minimum number threshold of distribution network communities in the first community structure, N max N represents the maximum number threshold of distribution network communities in the first community structure. f The first constraint is the cube root of the total number of nodes in the distribution network; the third constraint 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 community of the first community structure to be 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, the communication network adjacency matrix of the communication network is constructed according to the hierarchy of the communication network, comprising: dividing the communication network into an access layer, a backbone layer and a core layer according to the hierarchy of the communication network, the access layer being used for collecting information of the power distribution network and downward transmission of dispatching control signals, the backbone layer being used for interconnection between communities and forwarding information from the access layer, the core layer being interconnected with regional control centers in the backbone layer, comprising a main dispatching center and a backup dispatching center, and the core layer being used for information processing; constructing a communication network undirected graph G c = (V c , E c ) according to the network structure of the communication network, wherein V c is a set of nodes in the communication network, comprising information nodes in the access layer, regional control nodes in the backbone layer and dispatching center nodes of the core layer, E c is a 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 being equal to the number of nodes in the power distribution network, and the number of regional control nodes being equal to the number of power distribution network communities in the second community structure of the power distribution network; adding the information nodes in the communication network based on the carousel idea to form a final communication network community, the final communication network community being a region formed by the information nodes and the connection relationship between the information nodes, and the information nodes corresponding to each node in the power distribution network one by one; obtaining the communication network adjacency matrix from the final communication network community, wherein m is the total number of information nodes.

[0012] Optionally, the information nodes are added in the communication network based on the carousel idea to form a final communication network community, comprising: constructing a community preferential selection probability function wherein C i is the number of nodes of the i-th communication network community, and the communication network community corresponds to the power distribution network community one by one; constructing a node preferential connection probability function wherein δ Ai is the selection probability of node i in the A-th communication network community, k Ai is the number of edges of node i in the A-th communication network community, and k max is the maximum value of the number of edges of the nodes in the communication network community; constructing the final communication network community according to the community preferential selection probability function and the node preferential connection probability function.

[0013] Optionally, the coupling network adjacency matrix is constructed according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, including constructing the coupling network adjacency matrix according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. A is a coupling matrix used to describe the coupling relationship between the nodes in the power distribution network and the nodes in the communication network. p-c A is a coupling matrix used to describe the coupling relationship between the nodes in the power distribution network and the nodes in the communication network.

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

[0015] According to the technical solution of the application, in the dispatching method of the power distribution network, the power distribution network adjacency matrix and the power distribution network weight matrix are constructed according to the topology structure of the power distribution network, the power distribution network adjacency matrix is used to describe the connection relationship between nodes in the power distribution network, and the power distribution network weight matrix is used to describe the electrical coupling strength between nodes in the power distribution network; the power distribution network is divided into a plurality of power distribution network communities by using a Louvain community discovery algorithm according to the power distribution network weight matrix, a first community structure is obtained, the power distribution network community is a part of the power distribution network, the electrical distance between any two nodes in the power distribution network community is greater than a predetermined threshold, the first community structure is a domain composed of the power distribution network communities in the power distribution network, and the electrical distance is the change amount of the voltage amplitude or phase angle between nodes; a constraint condition is established to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint condition; a communication network adjacency matrix of the communication network is constructed according to the hierarchical structure of the communication network, the communication network adjacency matrix is used to describe the connection relationship between information nodes in the communication network; a coupling network adjacency matrix is constructed according to the power 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 power distribution network and the communication network; and power dispatching is performed on the power distribution network according to the coupling network adjacency matrix, so that the power supply and demand of the power distribution network is balanced. The electrical coupling strength of nodes in the power distribution network is obtained by constructing the power distribution network adjacency matrix and the power distribution network weight matrix, the nodes in the power distribution network are divided into communities according to the Louvain community discovery algorithm, the communication network adjacency matrix is constructed according to the hierarchical structure of the communication network, the coupling relationship between the power distribution network and the communication network is established through the power distribution network adjacency matrix and the communication network adjacency matrix, and the power dispatching personnel can make better decisions on the power dispatching of the power distribution network according to the coupling relationship, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the power distribution network and the communication network in the prior art. BRIEF DESCRIPTION OF DRAWINGS

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

[0017] Figure 2 A flowchart of a dispatching method of a power distribution network provided in an embodiment of the application is shown;

[0018] Figure 3 A structure block diagram of a dispatching device of a power distribution network provided in an embodiment of the application is shown.

[0019] Among them, the drawings include the following reference signs:

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

[0021] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0022] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background, the existing power distribution network is faced with the problems of load imbalance, difficult dispatching control, low network robustness, and no good contact with the communication layer. To solve the technical problem, the embodiments of the present application provide a power distribution network dispatching method and a dispatching device.

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

[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 the case of running on a mobile terminal, Figure 1 is a hardware structure block diagram of a mobile terminal of a power distribution network dispatching method according to an embodiment of the present application. As shown in Figure 1 , the mobile terminal can include one or more Figure 1The mobile terminal can further include a transmission device 106 for communication function and an input / output device 108. Those skilled in the art can understand that, Figure 1 The structure shown is only schematic and does not limit the structure of the mobile terminal. For example, the mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components. Figure 1 The mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components. Figure 1 The mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components.

[0027] The memory 104 can be used to store computer programs, such as software programs and modules of application software, and a computer program corresponding to the dispatching method of a power distribution network according to an embodiment of the present application. The processor 102 can execute various functional applications and data processing by running the computer program stored in the memory 104, i.e., implement the method described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely disposed relative to the processor 102, which can be connected to the mobile terminal through a network. Examples of the 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 specific example of the network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (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 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0028] In the present embodiment, a dispatching method of a power 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 is a flowchart of a dispatching method of a power distribution network according to an embodiment of the present application. As Figure 2 shown, the method includes the following steps:

[0030] In step S201, a power distribution network adjacency matrix and a power distribution network weight matrix are constructed according to a topology of the power distribution network, the power distribution network adjacency matrix is used to describe a connection relationship between nodes in the power distribution network, and the power distribution network weight matrix is used to describe an electrical coupling strength between the nodes in the power distribution network.

[0031] Specifically, the power distribution network adjacency matrix and the power distribution network weight matrix are constructed according to the topology of the power distribution network. The power distribution network adjacency matrix describes the connection relationship between the nodes in the power distribution network, so that it can be more clearly understood whether there is a connection between the nodes in the power distribution network. The power distribution network weight matrix describes the electrical coupling strength between the nodes in the power distribution network, so that it can be more clearly understood the coupling degree and the correlation strength between the nodes. This expression method is helpful for analyzing and optimizing the topology and electrical characteristics of the power distribution network.

[0032] In step S202, a Louvain community discovery algorithm is used to divide the power distribution network into a plurality of power distribution network communities according to the power distribution network weight matrix, to obtain a first community structure. The power distribution network community is a part of the power distribution network, and an electrical distance between any two nodes in the power distribution network community is greater than a predetermined threshold. The first community structure is a domain composed of the power distribution network communities in the power distribution network. The electrical distance is a change amount of a voltage amplitude or a phase angle between the nodes.

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

[0034] In step S203, a constraint condition is established to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint condition.

[0035] Specifically, the first community structure is adjusted according to the constraint condition to form the second community structure, so as to ensure that the second community structure satisfies the pre-set condition. This optimization process can help optimize the structure of the power distribution network, so that the community structure is more in line with the demand, and helps to improve the reliability of the power distribution network.

[0036] Step S204, constructing a communication network adjacency matrix of the communication network according to the hierarchy of the communication network, the communication network adjacency matrix being used to describe the connection relationship between the information nodes in the communication network;

[0037] Specifically, the communication network adjacency matrix of the communication network is constructed according to the hierarchy of the communication network, which can be used to describe the connection relationship between the information nodes in the communication network. Through the adjacency matrix, the association relationship between the information nodes in the communication network can be clearly shown, which is helpful for analyzing and understanding the topology of the communication network and can intuitively show the connection relationship between the information nodes in the communication network and the hierarchy between the nodes.

[0038] Step S205, constructing a coupling network adjacency matrix according to the power 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 power distribution network and the communication network;

[0039] Specifically, the coupling network adjacency matrix is constructed according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, which is used to describe the coupling relationship between the power distribution network and the communication network, indicating the mutual influence and association between the two, which is helpful for understanding the interaction and influence between the two and for optimizing the collaborative operation and management of the two network systems.

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

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

[0042] In the dispatching method of the power distribution network, the power distribution network adjacency matrix and the power distribution network weight matrix are constructed according to the topological structure of the power distribution network, the power distribution network adjacency matrix is used to describe the connection relationship between nodes in the power distribution network, and the power distribution network weight matrix is used to describe the electrical coupling strength between nodes in the power distribution network; the power distribution network is divided into a plurality of power distribution network communities by using a Louvain community discovery algorithm according to the power distribution network weight matrix, and a first community structure is obtained, the power distribution network community is a part of the power distribution network, and the electrical distance between any two nodes in the power distribution network community is greater than a predetermined threshold, the first community structure is a domain composed of each power distribution network community in the power distribution network, and the electrical distance is the change amount of voltage amplitude or phase angle between nodes; a constraint condition is established to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint condition; 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 information nodes in the communication network; a coupling network adjacency matrix is constructed according to the power 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 power distribution network and the communication network; and power dispatching is performed on the power distribution network according to the coupling network adjacency matrix, so that the power supply and demand of the power distribution network is balanced. The electrical coupling strength of each node in the power distribution network is obtained by constructing the power distribution network adjacency matrix and the power distribution network weight matrix, the nodes in the power distribution network are divided into communities according to the Louvain community discovery algorithm, the communication network adjacency matrix is constructed according to the hierarchical structure of the communication network, the coupling relationship between the power distribution network and the communication network is established through the power distribution network adjacency matrix and the communication network adjacency matrix, and the power dispatching personnel can make better decisions on the power dispatching of the power distribution network according to the coupling relationship, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the power distribution network and the communication network in the prior art.

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

[0044] In step S2011, a power distribution network undirected graph G p =(V p ,E p ,W p ) of the power distribution network is constructed according to the topological structure of the power distribution network, wherein V p is a set of nodes in the power distribution network, E p is a set of edges in the power distribution network, and W pThe set of weights of the edges in the power distribution network, the nodes are power equipment in the power distribution network, the edges are lines connecting the nodes in the power distribution network, and the weight of the edge is the electrical coupling strength between the nodes.

[0045] Specifically, a undirected graph is constructed according to the topology of the power distribution network, in which the nodes represent the power equipment in the power distribution network, the edges represent the lines connecting the equipment, and the weight of the edge represents the electrical coupling strength between the equipment. This representation of the graph helps to intuitively show the relationship and connection between the equipment in the power distribution network, and facilitates network analysis and optimization.

[0046] Step S2012, according to the topology of the power distribution network, the connection relationship between the nodes is determined, and the power distribution network adjacency matrix is obtained Wherein, a ij is the connection relationship between node v i and node v j , and n is the total number of nodes in the power distribution network.

[0047] Specifically, the connection between the nodes in the power distribution network is described by the power distribution network adjacency matrix, and each element represents the connection relationship between the nodes. If there is a connection between node v i and node v j , the value of the corresponding element of the power distribution network adjacency matrix is 1; if there is no connection, the value is 0. Through the power distribution network adjacency matrix, the connection relationship between the nodes in the power distribution network can be clearly shown.

[0048] Step S2013, according to the electrical distance, the power distribution network weight matrix is calculated Wherein, w ij is the weight of the edge between node v i and node v j , and the power distribution network weight matrix is used to describe the electrical coupling strength between the nodes.

[0049] Specifically, the power distribution network weight matrix is calculated according to the electrical distance, which describes the electrical coupling strength between the nodes. Each element represents the weight of the edge between the nodes, that is, the measure of the electrical coupling strength. Through the matrix, it is helpful to analyze the correlation and influence between the nodes in the power distribution network. The effect of this representation is that the electrical coupling strength between the nodes in the power distribution network can be clearly described by the weight matrix.

[0050] In order to determine the power distribution network weight matrix through the sensitivity matrix, in an optional embodiment, the power distribution network weight matrix is calculated according to the electrical distance, and the step S2013 comprises:

[0051] Step S20131, the active sensitivity and the reactive sensitivity are used to represent the above-mentioned 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;

[0052] Specifically, the active sensitivity and the reactive sensitivity are used to represent the electrical distance, wherein the active sensitivity represents 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 represents the influence of the reactive power change of the node on the voltage amplitude or phase angle change of other nodes. By representing the active and reactive sensitivities, the coupling relationship and the electrical distance between nodes can be more accurately described, and the electrical characteristics between different nodes in the power distribution network can be understood and optimized.

[0053] Step S20132, constructing a sensitivity matrix according to the active sensitivity and the reactive sensitivity Wherein, S Pδ is a phase angle active matrix, used to describe the influence of the change amount ΔP of the active power of each node on the corresponding node phase angle Δδ, S PV is a voltage amplitude active matrix, used to describe the influence of the change amount ΔP of the active power of each node on the corresponding node voltage amplitude ΔV, S Qδ is a phase angle reactive matrix, used to describe the influence of the change amount ΔP of the active power of each node on the corresponding node phase angle Δδ, S QV is a voltage amplitude reactive matrix, used to describe the influence of the change amount ΔP of the active power of each node on the corresponding node voltage amplitude ΔV, and the sensitivity matrix is used to describe the influence relationship between the power change and the voltage change of each node in the power distribution network.

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

[0055] Step S20133, calculating the power distribution network weight matrix according to the sensitivity matrix.

[0056] Specifically, the power distribution network weight matrix representing the electrical coupling strength between nodes in the power distribution network is calculated by using the sensitivity matrix. By calculating the sensitivity matrix, the influence of the node power change on the voltage change can be quantified, so as to determine the coupling strength between nodes and form the weight matrix.

[0057] To determine each element in the distribution network weight matrix, in one optional implementation, the distribution network weight matrix is ​​calculated based on the aforementioned sensitivity matrix. Step S20133 includes:

[0058] Step S201331: Calculate the reactive electrical distance between each node in the aforementioned distribution network based on the aforementioned sensitivity matrix. in, For node v j node v when reactive power changes j With node v i The ratio of the changes in voltage amplitude, S QV,ij The element in the i-th row and j-th column of the voltage amplitude reactive power matrix above represents node v. j Reactive power change at node v i The effect of voltage amplitude change, ΔV Qj For node v j The impact of reactive power change on voltage amplitude change;

[0059] Specifically, the reactive electrical distance between nodes in the distribution network is calculated using a sensitivity matrix, taking into account the impact of reactive power changes on voltage amplitude changes. By calculating the reactive electrical distance, the degree of impact of reactive power changes between nodes on voltage amplitude changes can be assessed, and the correlation between reactive power changes and voltage amplitude changes between nodes can be quantified. This helps to more accurately assess and manage the mutual influence of reactive power between nodes in the distribution network.

[0060] Step 201332: Calculate the active electrical distance between each node in the aforementioned distribution network based on the aforementioned sensitivity matrix. in, For node v j When active power changes, node v j With node v i The ratio of the changes in voltage amplitude, S PV,ij The element in the i-th row and j-th column of the above voltage magnitude active power matrix represents node v. j Changes in active power on node v i The effect of voltage amplitude change, ΔV Pj For node v j The impact of changes in active power on changes in voltage amplitude;

[0061] Specifically, the active electrical distance between each node in the power distribution network is calculated by using the sensitivity matrix, wherein the influence of the active power change on the voltage amplitude change is considered. By calculating the active electrical distance, the influence degree of the active power change between nodes on the voltage amplitude change can be evaluated, and the correlation degree between the active power change and the voltage amplitude change between nodes is quantified, which is helpful to more accurately evaluate and manage the mutual influence of active power between nodes in the power distribution network.

[0062] In step S201333, the electrical distance between each node is obtained according to the above-mentioned reactive electrical distance and the above-mentioned active electrical distance, and is

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

[0064] In step S201334, each element w in the power distribution network weight matrix is calculated according to the above-mentioned electrical distance. ij = (1-L ij ) / max(L).

[0065] Specifically, the value of each element in the power distribution network weight matrix is calculated by the electrical distance. According to the calculation result of the electrical distance, the coupling degree between nodes in the weight matrix is determined, and quantitative information about the interaction between nodes is provided.

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

[0067] In step S2021, each node in the power distribution network is taken as a community, and the current community of each node is obtained.

[0068] Specifically, each node in the power distribution network is taken as a community, and the current community of each node is obtained. This method regards each node as a whole, emphasizes the integrity of the node, and helps to provide a basis for the subsequent establishment of the community.

[0069] In step S2022, a first distribution step is used to distribute any target node in each node to the current community corresponding to the other nodes having a connection relationship with the target node, and the modularity gain of each current community is calculated wherein Σ in is the sum of the weights of all edges in the current community, and Σ totthe sum of the weights of the edges connecting the nodes in the current community and other communities, k i the sum of the weights of the edges connecting the nodes in the current community and other communities, k i the sum of the weights of the edges connecting the nodes in the current community and other communities, k i,in the sum of the weights of the edges connecting the nodes in the current community and other communities, k i the sum of the weights of the edges connecting the nodes in the current community and other communities, k

[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 strength of the connection within the community and the strength of the connection between communities. The calculation of the modularity gain provides a quantitative index for community optimization, which helps guide community allocation and structure adjustment.

[0071] Step S2023, a first determination step, is configured to determine a current community with the largest modularity gain according to the modularity gain of each current community, and determine the current community as the maximum gain community;

[0072] Specifically, in the first determination step, the modularity gains of each current community are compared to determine the current community with the largest modularity gain, which is determined as the maximum gain community. Determining the maximum gain community helps identify the most optimized part of the community structure, making the internal connection of the community more close.

[0073] Step S2024, a second allocation step, is configured to allocate the target node to the maximum gain community and update the nodes included in the current maximum gain community if the modularity gain of the maximum gain community is greater than 0.

[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 tightness and efficiency of the community.

[0075] Step S2025, a third allocation step, is configured not to allocate the target node if the modularity gain of the community with the largest modularity gain is less than 0.

[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 is not allocated. The purpose of this step is that when the modularity gain of the community with the largest modularity gain is negative, it is considered that the structure of the community is not ideal or optimized, so the target node is not selected for allocation.

[0077] Step S2026, first repeating step, repeating the above first allocation step, the above first determination step, the above second allocation step, the above third allocation step at least once in turn, until each of the above nodes no longer allocates, and the current community at this time is determined as a supernode, and a target network structure is formed according to the supernode;

[0078] Specifically, in the first repeating step, the first allocation step, the first determination step, the second allocation step and the third allocation step are executed in a loop at least once until all nodes no longer allocate, and the current community at this time is determined as a supernode. This iterative process continuously optimizes the community allocation, and finally forms a new network structure, in which the supernode represents an optimized node set. Through the iterative loop, the community structure is continuously optimized and adjusted, and the relevance between nodes is improved.

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

[0080] Specifically, in the second repeating step, the first allocation step, the first determination step, the second allocation step and the third allocation step are repeated in turn until the modularity gain of each current community in the target network structure no longer changes, and a first community structure is formed. Through the repeated steps of multiple iterations, the community structure can be effectively optimized to better adapt to and optimize the demand of the power distribution network, and the reliability of the system is improved.

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

[0082] Step S2031, first constraint condition, for setting at least one power supply node and one load node in each of the above communities in the above first community structure;

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

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

[0085] Specifically, the second constraint condition specifies the number of power grid communities in the first community structure, and such a constraint condition helps to control the number of communities, ensure the rationality and effectiveness of the community structure, and too many communities can make the model too complex and increase the computational burden; too few communities can make the model too rough and fail to accurately reflect the local characteristics of the network.

[0086] Step S2033, the 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 community in the first community structure to be between 0.6 and 1.5;

[0087] Specifically, the third constraint condition specifies that the ratio of the total power of the load nodes to the total power of the power supply nodes in each community in the first community structure should be between 0.6 and 1.5, and this constraint condition helps to maintain the balance between supply and demand within each community and ensure the reasonable matching between loads and power supplies, thereby ensuring the stable operation of the power grid and the balance of power supply loads.

[0088] Step S2034, optimizing 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 constraint conditions, a second community structure that is adjusted and improved is obtained, so that the second community structure is more reasonable and optimized under the premise of meeting the constraints, and 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, 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 for collecting information of the power grid and downward transmitting 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 centers in the backbone layer, including a main dispatching center and a backup dispatching 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 power 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 standby dispatching center, responsible for information processing, this hierarchical design ensures the transmission of information flow, and realizes the efficient cooperation of physical and information layers in the smart grid.

[0093] Step S2042, constructing a communication network undirected graph G according to the network structure of the communication network c = (V c , E c ), wherein V c is a set of nodes in the communication network, including information nodes in the access layer, regional control nodes in the backbone layer, and dispatching center nodes in the core layer, E c is a set of edges in the communication network.

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

[0095] Step S2043, 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 power distribution network, and the number of regional control nodes is equal to the number of power distribution network communities in the second community structure in the power distribution network.

[0096] Specifically, the number of information nodes in the communication network is ensured to be one-to-one corresponding to the nodes in the power distribution network, and the number of deployed regional control nodes is also accurately matched with the number of communities in the power distribution network.

[0097] Step S2044, adding the information nodes in the communication network based on the carousel idea to form the final communication network community, the final communication network community is a region composed of the information nodes and the connection relationship between the information nodes, and the information nodes are one-to-one corresponding to the nodes in the power distribution network.

[0098] Specifically, by using the random selection mechanism of the carousel, we integrate the information nodes into the communication network to form the final communication community structure directly mapped with the nodes in the power distribution network. Through this node adding strategy and community construction method, we realize the accurate docking of the communication network and the power distribution network.

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

[0100] Specifically, according to the final communication network community constructed by us, the communication network adjacency matrix is determined, and through the communication network adjacency matrix, the association relationship of each information node can be determined, and a good connection is established for each information node.

[0101] In order to obtain the final communication network community, in an optional implementation, the above information node is added in the above communication network based on the carousel idea to form the final communication network community, and the above step S2044 includes:

[0102] Step S20441, constructing a community priority selection probability function Wherein, C i The number of nodes of the i-th communication network community, the communication network community corresponds to the power distribution network community one by one;

[0103] Specifically, through the community priority selection probability function, the communication network community can be preferentially selected, and the calculation method of the probability function is determined according to the number of nodes of each communication network community. Each communication network community corresponds to a power distribution network community. When selecting the community, the number of nodes of the communication network community is preferentially considered to ensure that the communication demand can be better met when constructing the network.

[0104] Step S20442, constructing a node priority connection probability function Wherein, δ Ai The probability of selecting node i in the A-th communication network community, k Ai The number of edges of node i in the A-th communication network community, k max The maximum value of the number of edges of the node in the communication network community;

[0105] Specifically, the node priority connection probability function is constructed, which can make the priority order of node connection more reasonable and efficient. By considering the number of edges of the node and the maximum value of the number of edges of the node in the whole community, the importance of the node and the tightness of the connection can be better measured, so as to realize a more effective node connection strategy.

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

[0107] Specifically, through the community priority selection probability function and the node priority connection probability function, we construct a final communication network community. Through the two functions, the nodes can be effectively organized into communities, so as to improve the efficiency and performance of the network, and form a more compact community structure.

[0108] To obtain the coupling network adjacency matrix, in an optional implementation, the coupling network adjacency matrix is constructed according to the power 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 power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix Wherein, A p-c is an association matrix used to describe the coupling relationship between the nodes in the power distribution network and the nodes in the communication network.

[0110] Specifically, the coupling network adjacency matrix is constructed according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. The coupling network adjacency matrix describes the coupling relationship between the nodes in the power distribution network and the nodes in the communication network. Through the coupling network adjacency matrix, the interaction and influence between the power distribution network and the communication network can be more clearly understood.

[0111] 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 the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0112] The embodiments of the present application also provide a power distribution network scheduling device. It should be noted that the power distribution network scheduling device of the embodiments of the present application can be used to execute the power distribution network scheduling method provided by the embodiments of the present application. The device is used to implement the above embodiments and preferred embodiments, which have been described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware, or a combination of software and hardware can also be implemented and conceived.

[0113] The power distribution network scheduling device provided by the embodiments of the present application is described below.

[0114] Figure 3 is a structural block diagram of a power distribution network scheduling device according to the embodiments of the present application. As Figure 3 shown, the device includes:

[0115] The first establishment unit 10 is configured to construct a power distribution network adjacency matrix and a power distribution network weight matrix according to the topology structure of the power distribution network. The power distribution network adjacency matrix is used to describe the connection relationship between the nodes in the power distribution network. The power distribution network weight matrix is used to describe the electrical coupling strength between the nodes in the power distribution network.

[0116] The first division unit 20 is configured to divide the power distribution network into a plurality of power distribution network communities according to the weight matrix of the power distribution network by using a Louvain community detection algorithm, to obtain a first community structure, wherein each power distribution network community is a part of the power distribution network, and an electrical distance between any two nodes in the power distribution network community is greater than a predetermined threshold, the first community structure is a domain composed of each power distribution network community in the power distribution network, and the electrical distance is a variation of voltage amplitude or phase angle between each node;

[0117] The first constraint unit 30 is configured to establish a constraint condition to optimize the first community structure, to obtain a second community structure, so that the second community structure satisfies the constraint condition;

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

[0119] The third establishment unit 50 is configured to construct 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 to describe a coupling relationship between the power distribution network and the communication network;

[0120] The first control unit 60 is configured to perform power dispatching on the power distribution network according to the coupling network adjacency matrix, so that power supply and demand of the power distribution network are balanced.

[0121] By the embodiment, the first establishing unit is configured to construct a power distribution network adjacency matrix and a power distribution network weight matrix according to a topology structure of the power distribution network, the power distribution network adjacency matrix is configured to describe a connection relationship between nodes in the power distribution network, and the power distribution network weight matrix is configured to describe an electrical coupling strength between the nodes in the power distribution network; the first dividing unit is configured to divide the power distribution network into a plurality of power distribution network communities according to the power distribution network weight matrix by using a Louvain community discovery algorithm to obtain a first community structure, the power distribution network community is a part of the power distribution network, an electrical distance between any two nodes in the power distribution network community is greater than a predetermined threshold, the first community structure is a domain composed of the power distribution network communities in the power distribution network, and the electrical distance is a variation of a voltage amplitude or a phase angle between the nodes; the first constraint unit is configured to establish a constraint condition to optimize the first community structure to obtain a second community structure, so that the second community structure satisfies the constraint condition; the second establishing unit is configured to construct a communication network adjacency matrix of the communication network according to a hierarchical structure of the communication network, the communication network adjacency matrix is configured to describe a connection relationship between information nodes in the communication network; the third establishing unit is configured to construct a coupling network adjacency matrix according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, the coupling network adjacency matrix is configured to describe a coupling relationship between the power distribution network and the communication network; and the first control unit is configured to perform power dispatching on the power distribution network according to the coupling network adjacency matrix, so that power supply and demand of the power distribution network are balanced. The application obtains the electrical coupling strength of the nodes in the power distribution network by constructing the power distribution network adjacency matrix and the power distribution network weight matrix, divides the nodes in the power distribution network into communities according to the Louvain community discovery algorithm, constructs the communication network adjacency matrix according to the hierarchical structure of the communication network, establishes the coupling relationship between the power distribution network and the communication network by the power distribution network adjacency matrix and the communication network adjacency matrix, and makes a better decision on the power dispatching of the power distribution network according to the coupling relationship, thereby solving the problem of low power dispatching efficiency caused by the low coupling relationship between the power distribution network and the communication network in the prior art.

[0122] In order to obtain the power distribution network adjacency matrix and the power distribution network weight matrix, in an optional embodiment, the first establishing unit includes:

[0123] The first establishing subunit is configured to construct a power distribution network undirected graph G p = (V p , E p , W p ) of the power distribution network according to the topology structure of the power distribution network, where V p is a set of nodes in the power distribution network, E pA set of weights of edges in the power distribution network, the nodes are power equipment in the power distribution network, the edges are lines connecting the nodes in the power distribution network, and the weights of the edges are the electrical coupling strengths between the nodes. p A set of weights of edges in the power distribution network, the nodes are power equipment in the power distribution network, the edges are lines connecting the nodes in the power distribution network, and the weights of the edges are the electrical coupling strengths between the nodes.

[0124] Specifically, a undirected graph is constructed according to the topology of the power distribution network, in which the nodes represent the power equipment in the power distribution network, the edges represent the lines connecting the equipment, and the weights of the edges represent the electrical coupling strengths between the equipment. This representation of the graph helps to intuitively show the relationship and connection between the equipment in the power distribution network, and facilitates network analysis and optimization.

[0125] The second establishing subunit is configured to determine the connection relationship between the nodes according to the topology of the power distribution network, and obtain the power distribution network adjacency matrix wherein a ij is the connection relationship between node v i and node v j , and n is the total number of nodes in the power distribution network.

[0126] Specifically, the connection between the nodes in the power distribution network is described by the power distribution network adjacency matrix, wherein each element represents the connection relationship between the nodes. If there is a connection between node v i and node v j , the value of the corresponding element of the power distribution network adjacency matrix is 1; if there is no connection, the value is 0. Through the power distribution network adjacency matrix, the connection relationship between the nodes in the power distribution network can be clearly shown.

[0127] The third establishing subunit is configured to calculate the power distribution network weight matrix wherein w ij is the weight of the edge between node v i and node v j , and the power distribution network weight matrix is used to describe the electrical coupling strength between the nodes.

[0128] Specifically, the power distribution network weight matrix is calculated according to the electrical distance, which describes the electrical coupling strength between the nodes. Each element represents the weight of the edge between the nodes, i.e. the measure of the electrical coupling strength. Through the matrix, it is helpful to analyze the correlation and influence between the nodes in the power distribution network. The effect of this representation is that the electrical coupling strength between the nodes in the power distribution network can be clearly described by the weight matrix.

[0129] In order to determine the power distribution network weight matrix through the sensitivity matrix, in an optional implementation, the power distribution network weight matrix is calculated according to the electrical distance, and the third establishing module comprises:

[0130] The first establishing module is configured to represent the electrical distance by using active sensitivity and reactive sensitivity, the active sensitivity is the influence of active power change of a node on voltage amplitude or phase angle change of other nodes, and the reactive sensitivity is the influence of reactive power change of a node on voltage amplitude or phase angle change of other nodes.

[0131] Specifically, the electrical distance is represented by using active sensitivity and reactive sensitivity, the active sensitivity represents the influence of active power change of a node on voltage amplitude or phase angle change of other nodes, and the reactive sensitivity represents the influence of reactive power change of a node on voltage amplitude or phase angle change of other nodes, by representing the active and reactive sensitivity, the coupling relationship and the electrical distance between nodes can be more accurately described, and the electrical characteristics between different nodes in the power distribution network can be understood and optimized.

[0132] The second establishing module is configured to construct a sensitivity matrix according to the active sensitivity and the reactive sensitivity. Wherein, S Pδ is a phase angle active matrix, used to describe the influence of the change amount ΔP of active power of each node on the phase angle Δδ of the corresponding node, S PV is a voltage amplitude active matrix, used to describe the influence of the change amount ΔP of active power of each node on the voltage amplitude ΔV of the corresponding node, S Qδ is a phase angle reactive matrix, used to describe the influence of the change amount ΔP of reactive power of each node on the phase angle Δδ of the corresponding node, S QV is a voltage amplitude reactive matrix, used to describe the influence of the change amount ΔP of reactive power 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 amount and the voltage change amount of each node in the power distribution network.

[0133] Specifically, the sensitivity matrix is constructed according to the active and reactive sensitivity, which includes the phase angle active matrix, the voltage amplitude active matrix, the phase angle reactive matrix and the voltage amplitude reactive matrix, these matrices describe the influence relationship of node power change on the phase angle and voltage amplitude of the corresponding node, through the sensitivity matrix, the correlation between the node power change and the voltage change 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 configured to calculate the power distribution network weight matrix according to the sensitivity matrix.

[0135] Specifically, the power distribution network weight matrix representing the electrical coupling strength between nodes in the power distribution network is calculated by using the sensitivity matrix. By calculating the sensitivity matrix, the influence of the node power change on the voltage change can be quantified, so as to determine the coupling strength between nodes and form the weight matrix.

[0136] To determine each element in the power distribution network weight matrix, in an optional implementation, the power distribution network weight matrix is calculated according to the sensitivity matrix, and the third establishing module comprises:

[0137] A first establishing submodule is configured to calculate the reactive electrical distance between each node in the power distribution network according to the sensitivity matrix. Wherein, When the reactive power of node v j changes, the ratio of the voltage amplitude change of node v j to the reactive power change of node v i is S QV,ij , which is the element in the i-th row and the j-th column of the voltage amplitude reactive matrix, representing the influence of the reactive power change of node v j on the voltage amplitude change of node v i , ΔV Qj is the influence of the reactive power change of node v j on the voltage amplitude change of node v j .

[0138] Specifically, the reactive electrical distance between nodes in the power distribution network is calculated by using the sensitivity matrix, wherein the influence of the reactive power change on the voltage amplitude change is considered. By calculating the reactive electrical distance, the influence degree of the reactive power change between nodes on the voltage amplitude change can be evaluated, and the correlation degree between the reactive power change and the voltage amplitude change between nodes is quantified, which is helpful to more accurately evaluate and manage the mutual influence of the reactive power between nodes in the power distribution network.

[0139] A second establishing submodule is configured to calculate the active electrical distance between each node in the power distribution network according to the sensitivity matrix. Wherein, When the active power of node v j changes, the ratio of the voltage amplitude change of node v j to the active power change of node v i is S PV,ij , which is the element in the i-th row and the j-th column of the voltage amplitude active matrix, representing the influence of the active power change of node v j on the voltage amplitude change of node v i , ΔV Pj is the influence of the active power change of node v jInfluence of active power change amount on voltage amplitude change amount;

[0140] Specifically, the active electrical distance between each node in the power distribution network is calculated by using the sensitivity matrix, wherein the influence of active power change on voltage amplitude change is considered. By calculating the active electrical distance, the influence degree of active power change between nodes on voltage amplitude change can be evaluated, and the correlation degree between active power change and voltage amplitude change between nodes is quantified, which is helpful for more accurately evaluating and managing the mutual influence of active power between nodes in the power distribution network.

[0141] The third establishing sub-module is configured to obtain the electrical distance between each node 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 comprehensively considers the influence of node power change on voltage change and quantifies the electrical coupling relationship between nodes.

[0143] The third establishing sub-module is configured to obtain the electrical distance between each node according to the reactive electrical distance and the active electrical distance. ij =(1-L ij ) / max(L).

[0144] Specifically, the value of each element in the weight matrix is calculated by the electrical distance. According to the calculation result of the electrical distance, the coupling degree between nodes in the weight matrix is determined, and quantitative information about the interaction between nodes is provided.

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

[0146] The first division module is configured to take each node in the power distribution network as a community to obtain the current community of each node.

[0147] Specifically, each node in the power distribution network is taken as a community to obtain the current community of each node. This method 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 division module is configured to perform the first allocation step, and is configured to allocate any target node in each node to the current community corresponding to the other nodes having a connection relationship with the target node, and calculate the modularity gain of each current community. Σ inis the sum of the weights of all edges in the current community in the current community tot is the sum of the weights of edges connecting nodes in the current community and other communities, k i is the sum of the weights of edges connecting nodes connected to node v i is the sum of the weights of edges connecting nodes connected to node v i,in is the sum of the weights of edges connecting nodes connected to node v i is the sum of the weights of edges within the current community, m is the sum of the weights of all edges in the power 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 strength of the connection within the community and the strength of the connection between communities. The calculation of the modularity gain provides a quantitative index for community optimization, which helps guide community allocation and structure adjustment.

[0150] The third division module is configured to perform the first determination step, and is configured to determine the current community with the maximum modularity gain according to the modularity gain of each current community, and determine the current community as the maximum gain community;

[0151] Specifically, in the first determination step, the modularity gains of each current community are compared to determine the current community with the maximum modularity gain, and the current community is 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 more close.

[0152] The fourth division module is configured to perform the second allocation step, and is configured to allocate the target node to the maximum gain community and update the nodes included in the current maximum gain community if the modularity gain of the maximum gain community is greater than 0.

[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, so as to optimize the community structure and improve the tightness and efficiency of the community.

[0154] The fifth division module is configured to perform the third allocation step, and is configured to not allocate the target node if the modularity gain of the community with the maximum modularity gain is less than 0.

[0155] Specifically, in the third allocation step, if the modularity gain of the community with the maximum modularity gain is less than 0, the target node is not allocated. The purpose of this step is that when the modularity gain of the community with the maximum modularity gain is negative, it is considered that the structure of the community is not ideal or optimized, so the target node is not selected to be allocated.

[0156] The sixth division module is configured to perform a first repeating step, and sequentially repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once until each node no longer performs allocation, and determine each current community at this time as a supernode, and form a target network structure according to the supernodes;

[0157] Specifically, in the first repeating step, the first allocation step, the first determination step, the second allocation step, and the third allocation step are cyclically executed at least once until all nodes no longer perform allocation, and each current community at this time is determined as a supernode. This iterative process continuously optimizes the community allocation, and finally forms a new network structure, in which the supernodes represent an optimized node set. Through the iterative loop, the community structure is continuously optimized and adjusted, and the relevance between nodes is improved.

[0158] The seventh division module is configured to perform a second repeating step, and sequentially repeat the first allocation step, the first determination step, the second allocation step, and the third allocation step at least once until the modularity gain of each current community in the target network structure no longer changes, and obtain a first community structure.

[0159] Specifically, in the second repeating step, the first allocation step, the first determination step, the second allocation step, and the third allocation step are sequentially repeated until the modularity gain of each current community in the target network structure no longer changes, and a first community structure is formed. Through the repeated steps of multiple iterations, the community structure can be effectively optimized to better adapt to and optimize the demand of the power distribution network, and the reliability of the system is improved.

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

[0161] The first constraint module is configured to perform a first constraint condition, and is configured to set at least one power node and one load node in each community in the first community structure.

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

[0163] The second constraint module is configured to perform a second constraint condition, and is configured to set the number of power distribution network communities in the first community structure as max(N min ,N f -2)≤n≤min(N max ,Nf ), wherein Nmin is a minimum number threshold of the power grid community in the first community structure, N max max is a maximum number threshold of the power grid community in the first community structure, N f is a cubic root of the total number of the nodes in the power grid;

[0164] Specifically, the second constraint condition specifies the number of power grid communities in the first community structure, and such a constraint condition helps to control the number of communities, guarantee the rationality and effectiveness of the community structure, and too many communities can make the model too complex and increase the computational burden, and too few communities can make the model too rough and fail to accurately reflect the local characteristics of the network.

[0165] The third constraint module is configured to execute a third constraint condition, which is configured to set the ratio of the total power of the load nodes to the total power of the power supply nodes in each community in the first community structure to be between 0.6 and 1.5.

[0166] Specifically, the third constraint condition specifies that the ratio of the total power of the load nodes to the total power of the power supply nodes in each community in the first community structure should be between 0.6 and 1.5, and this constraint condition helps to maintain the balance between supply and demand within each community and ensure the reasonable matching between the load and the power supply, thereby guaranteeing the stable operation of the power grid and the balance of power supply load.

[0167] The fourth constraint module is configured 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 constraint conditions, a second community structure that is adjusted and improved is obtained, so that the second community structure is more reasonable and optimized under the premise of meeting the constraint conditions, and 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, 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 comprises:

[0170] The first establishing sub-unit is configured 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, the access layer is configured to collect information of the power grid and downward transmit control signals, the backbone layer is configured to interconnect communities and forward information from the access layer, the core layer is interconnected with regional control centers in the backbone layer and comprises a main dispatching center and a backup dispatching center, and the core layer is configured to process information.

[0171] Specifically, according to the hierarchy 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 power 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 standby dispatching center, responsible for information processing, this hierarchical design ensures the transmission of information flow, and realizes the efficient cooperation of physical and information layers in the smart grid.

[0172] The fifth establishment subunit is configured to construct a communication network undirected graph G c =(V c ,E c ) according to the network structure of the communication network, wherein V c is a set of nodes in the communication network, including information nodes in the access layer, regional control nodes in the backbone layer, and dispatching center nodes in the core layer, E c is a set of edges in the communication network.

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

[0174] The sixth establishment subunit is configured to determine 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 power distribution network, and the number of regional control nodes is equal to the number of power distribution network communities in the second community structure in the power distribution network.

[0175] Specifically, the number of information nodes in the communication network is ensured to be one-to-one corresponding to the nodes in the power distribution network, and the number of deployed regional control nodes is also accurately matched with the number of communities in the power distribution network.

[0176] The seventh establishment subunit is configured to add the information nodes in the communication network based on the carousel idea to form a final communication network community, the final communication network community is a region formed by the information nodes and the connection relationship between the information nodes, and the information nodes are one-to-one corresponding to the nodes in the power distribution network.

[0177] Specifically, by using the random selection mechanism of the carousel, the information nodes are integrated into the communication network to form a final communication community structure directly mapped with the nodes in the power distribution network. Through this node adding strategy and community construction method, the accurate connection between the communication network and the power distribution network is realized.

[0178] The eighth establishment subunit is configured to obtain the communication network adjacency matrix wherein m is the total number of information nodes.

[0179] Specifically, according to the final communication network community constructed by us, the communication network adjacency matrix is determined, and through the communication network adjacency matrix, the association relationship of each information node is determined, and a good connection is established for each information node.

[0180] In order to obtain the final communication network community, in an optional implementation, the above information nodes are added in the above communication network based on the carousel idea to form the final communication network community, and the seventh establishment subunit includes:

[0181] The first establishment module is configured to construct a community priority selection probability function wherein C i is the number of nodes of the i-th communication network community, and the communication network community corresponds to the power distribution network community one by one;

[0182] Specifically, through the community priority selection probability function, the communication network community can be preferentially selected, and the calculation method of the probability function is determined according to the number of nodes of each communication network community. Each communication network community corresponds to a power distribution network community. The number of nodes of the communication network community is preferentially considered when selecting the community, so as to ensure that the communication demand can be better met when constructing the network.

[0183] The fifth establishment module is configured to construct a node priority connection probability function wherein δ Ai is the selection probability of node i in the A-th communication network community, k Ai is the number of edges of node i in the A-th communication network community, and k max is the maximum value of the number of edges of the nodes in the communication network community.

[0184] Specifically, the node priority connection probability function is constructed, so that the priority order of node connection is more reasonable and efficient. By considering the number of edges of the node and the maximum value of the number of edges of the nodes in the whole community, the importance of the node and the closeness of the connection can be better measured, so as to realize a more effective node connection strategy.

[0185] The sixth establishment module is configured to construct the final communication network community according to the community priority selection probability function and the node priority connection probability function.

[0186] Specifically, through the community priority selection probability function and the node priority connection probability function, a final communication network community is constructed, and through the two functions, the nodes can be effectively organized into communities, so as to improve the efficiency and performance of the network, and form a more close community structure.

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

[0188] A ninth establishing sub-unit is configured to construct the coupling network adjacency matrix according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. Wherein, A p-c is an association matrix configured to describe the coupling relationship between the nodes in the power distribution network and the nodes in the communication network.

[0189] Specifically, the coupling network adjacency matrix is constructed according to the power distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix, which describes the coupling relationship between the nodes in the power distribution network and the nodes in the communication network, and through the coupling network adjacency matrix, the interaction and influence between the power distribution network and the communication network can be more clearly understood.

[0190] The dispatching device of the power distribution network comprises a processor and a memory, and the first establishing unit, the first dividing unit, the first constraint unit, the second establishing unit, the third establishing unit, the first control unit, etc. are stored in the memory as program units, and the corresponding functions are realized by the processor executing the program units stored in the memory. The modules are located in the same processor; or, the modules are located in different processors in any combination. The processor contains a core, and the core retrieves the corresponding program units from the memory. The core can be set to one or more, and the power dispatching efficiency of the power distribution network can be improved by adjusting the core parameters. The memory can include non-persistent memory in a computer readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

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

[0192] 1) The dispatching method of the power distribution network of the present application obtains the electrical coupling strength of each node in the power distribution network by constructing the power distribution network adjacency matrix and the power distribution network weight matrix, divides the communities of each node in the power distribution network according to the Louvain community discovery algorithm, constructs the communication network adjacency matrix according to the hierarchical structure of the communication network, establishes the coupling relationship between the power distribution network and the communication network through the power distribution network adjacency matrix and the communication network adjacency matrix, and the dispatching personnel can make better decisions on the power dispatching of the power distribution network according to the coupling relationship, solving 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.

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

[0194] The above only describes the preferred embodiments of the application and is not intended to limit the application. Those skilled in the art can make various modifications and changes to the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for dispatching a power distribution network, characterized in that, include: Based on the topology of the distribution network, a distribution network adjacency matrix and a distribution network weight matrix are constructed. 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 Louvain community detection algorithm is used to divide the distribution network into multiple distribution network communities based on the distribution network weight matrix to obtain a first community structure. 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. The electrical distance is the change in voltage amplitude or phase angle between the nodes. Establish constraints to optimize the first community structure to obtain a second community structure, such that the second community structure satisfies the constraints. The communication network adjacency matrix is ​​constructed based on the hierarchical structure of the communication network. The communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network. A coupled network adjacency matrix is ​​constructed based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. The coupled network adjacency matrix is ​​used to describe the coupling relationship between the distribution network and the communication network. Power dispatching is performed on the distribution network based on the adjacency matrix of the coupled network to achieve a balance between power supply and demand in the distribution network. The Louvain community discovery algorithm is used to divide the distribution network into multiple distribution network communities based on the distribution network weight matrix, resulting in a first community structure. This includes: classifying each node in the distribution network as a community to obtain the current community of each node; and a first allocation step, used to allocate any target node among the nodes to the current community corresponding to other nodes connected to the target node, and calculating the modularity gain of each current community. ,in, This is the sum of the weights of all edges in the current community. This is the sum of the weights of the edges connecting nodes in the current community to nodes in other communities. For nodes The sum of the weights of the edges connecting the nodes. For nodes The sum of the weights of the edges within the current community. The first determination step is used to determine the current community with the largest modularity gain based on the modularity gain of each current community, and determine it as the maximum gain community; the second allocation step is used to allocate the target node to the maximum gain community if the modularity gain of the maximum gain community is greater than 0, and update the nodes included in the current maximum gain community; the third allocation step is used to not allocate the target node if the modularity gain of the community with the largest modularity gain is less than 0; the 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 node is no longer allocated, and each current community at this time is determined as a super node, and the target network structure is formed based on the super nodes; the 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 current community in the target network structure no longer changes, and the first community structure is obtained.

2. The method according to claim 1, characterized in that, Based on the distribution network topology, construct the distribution network adjacency matrix and distribution network weight matrix, including: Construct an undirected graph of the distribution network based on the topology of the distribution network. ,in, It is the set of nodes in the power distribution network. The set consisting of all the edges in the power distribution network. The set of weights of each edge in the power distribution network, where each node is a power device in the power distribution network, each edge is a line connecting each node in the power distribution network, and the weight of each edge is the electrical coupling strength between each node; The connection relationships between the nodes are determined based on the topology of the distribution network, thus obtaining the distribution network adjacency matrix. ,in, For nodes With nodes The connection between them The total number of nodes in the power distribution network; The distribution network weight matrix is ​​calculated based on electrical distance. ,in, For nodes With nodes The weights of the edges between the nodes are defined in the weight matrix, which 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 ​​calculated based on electrical distance, including: The electrical distance is represented by active power sensitivity and reactive power sensitivity. The active power sensitivity is the effect of the change in active power of a node on the voltage amplitude or phase angle of other nodes, and the reactive power sensitivity is the effect of the change in reactive power of a node on the voltage amplitude or phase angle of other nodes. Construct a sensitivity matrix based on the active power sensitivity and the reactive power sensitivity. ,in, This is the phase angle active power matrix, used to describe the change in active power at each of the aforementioned nodes. Phase angle of the corresponding node The impact, This is the voltage magnitude active power matrix, used to describe the change in active power at each node. For the corresponding node voltage amplitude The impact, This is the phase angle reactive power matrix, used to describe the change in reactive power at each node. Phase angle of the corresponding node The impact, This is the voltage magnitude reactive power matrix, used to describe the change in reactive power at each node. For the corresponding node voltage amplitude The sensitivity matrix is ​​used to describe the influence relationship between the power change and voltage change at each node in the distribution network. The power distribution network weight matrix is ​​calculated based on the sensitivity matrix.

4. The method according to claim 3, characterized in that, The distribution network weight matrix is ​​calculated based on the sensitivity matrix, including: The reactive electrical distance between each node in the distribution network is calculated based on the sensitivity matrix. ,in, , i=1, 2…n, j=1, 2…n, For the node Nodes when reactive power changes With nodes The ratio of the changes in voltage amplitude The voltage amplitude reactive power matrix is ​​the first... Line number The elements of a column represent nodes. Reactive power change at nodes The effect of voltage amplitude change, For nodes The impact of reactive power change on voltage amplitude change; The active electrical distance between each node in the distribution network is calculated based on the sensitivity matrix. ,in, , i=1, 2…n, j=1, 2…n, For the node When active power changes, the node With nodes The ratio of the changes in voltage amplitude The first active power matrix of voltage amplitude is the... Line number The elements of a column represent nodes. Changes in active power at nodes The effect of voltage amplitude change, For nodes The impact of changes in active power on changes in voltage amplitude; The electrical distance between each node is obtained based on the reactive electrical distance and the active electrical distance. ; The elements in the distribution network weight matrix are calculated based on the electrical distance. .

5. The method according to claim 1, characterized in that, Establish constraints to optimize the first community structure, resulting in a second community structure, including: The first constraint condition is used to set that each community in the first community structure includes 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. ,in, This is the minimum number threshold of distribution network communities in the first community structure. This is the maximum threshold number of distribution network communities in the first community structure. It is the cube root of the total number of nodes in the distribution network; The third constraint is used to set the ratio of the total power of the load nodes to the total power of the power 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 based on the first constraint, the second constraint, and the third constraint to obtain the second community structure.

6. The method according to claim 2, characterized in that, The communication network adjacency matrix of the communication network is constructed based on 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 from the distribution network and transmit dispatch control signals. The backbone layer is used for interconnection between communities and to forward information from the access layer. The core layer is interconnected with the regional control centers in the backbone layer, including a main dispatch center and a backup dispatch center. The core layer is used for information processing. Construct an undirected graph of the communication network based on its network structure. ,in, This refers to the collection of nodes in the communication network, including information nodes in the access layer, area control nodes in the backbone layer, and the scheduling center node in the core layer. The set of edges in the communication network; The number of information nodes and the number of area control nodes in the communication network are determined, wherein the number of information nodes in the communication network is equal to the number of nodes in the distribution network, and the number of area control nodes is equal to the number of distribution network communities in the second community structure of the distribution network; Based on the concept of a turntable, information nodes are added to the communication network to form a final communication network community. The final communication network community is the area formed by the information nodes and the connection relationships between them. Each information node corresponds one-to-one with each node in the distribution network. The adjacency matrix of the communication network is obtained based on the final communication network community. ,in, This represents the total number of information nodes.

7. The method according to claim 6, characterized in that, Constructing a coupled network adjacency matrix based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix includes: The coupled network adjacency matrix is ​​constructed based on the distribution network adjacency matrix, the second community structure, and the communication network adjacency matrix. ,in, This is an incidence matrix used to describe the coupling relationship between nodes in the power distribution network and nodes in the communication network.

8. A dispatching device for a power distribution network, characterized in that, include: The first establishment unit is used to construct a distribution network adjacency matrix and a distribution network weight matrix according to the topology of the distribution network. 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 first partitioning unit is used to divide the distribution network into multiple distribution network communities according to the distribution network weight matrix using the Louvain community discovery algorithm to obtain a first community structure. 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 each distribution network community in the distribution network, and the electrical distance is the change in voltage amplitude or phase angle between each node. The first constraint unit is used to establish constraint conditions to optimize the first community structure and obtain a second community structure, such that the second community structure satisfies the constraint conditions. The second establishment unit is used to construct the communication network adjacency matrix of the communication network according to the hierarchical structure of the communication network. The communication network adjacency matrix is ​​used to describe the connection relationship between each information node in the communication network. The third establishment unit is used to establish a coupling network adjacency matrix based on 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 first control unit is used to perform power dispatching on the distribution network according to the adjacency matrix of the coupled network, so as to achieve a balance between power supply and demand in the distribution network. The first partitioning unit includes: a first partitioning module, used to classify each node in the distribution network as a community to obtain the current community of each node; and a second partitioning module, used to execute a first allocation step, used to allocate any target node among the nodes to the current community corresponding to other nodes that have a connection relationship with the target node, and to calculate the modularity gain of each current community. ,in, This is the sum of the weights of all edges in the current community. This is the sum of the weights of the edges connecting nodes in the current community to nodes in other communities. For nodes The sum of the weights of the edges connecting the nodes. For nodes The sum of the weights of the edges within the current community. The first partitioning module is the sum of the weights of all edges in the distribution network; the second partitioning module is used to execute the first determination step, which determines the current community with the largest modularity gain based on the modularity gain of each current community, and determines it as the maximum gain community; the third partitioning module is used to execute the second allocation step, which allocates the target node to the maximum gain community and updates the nodes included in the current maximum gain community if the modularity gain of the maximum gain community is greater than 0; the fourth partitioning module is used to execute the third allocation step, which does not allocate the target node if the modularity gain of the community with the largest modularity gain is less than 0. The sixth partitioning module is used to execute the first repeating 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 at this time as a super node, and forming a target network structure based on the super nodes; the seventh partitioning module is used to execute the second repeating 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 module degree gain of each of the current communities in the target network structure no longer changes, and obtaining the first community structure.

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