Vulnerability optimization method for power supply area nodes

Abstract

The invention relates to the technical field of power supply optimization, in particular to a vulnerability optimization method for nodes in a power supply area. According to the method, a vulnerability evaluation index based on a complex network theory and a risk evaluation index based on a risk theory are firstly carried out; performing weighted summation on the obtained node vulnerability indexes through an analytic hierarchy process, and determining a vulnerability comprehensive evaluation index of the power distribution network nodes based on a summation result and the risk index of each node; calculating an actual value of a vulnerability comprehensive evaluation index of the power distribution network node; based on the actual value of the vulnerability comprehensive evaluation index of the power distribution network node, determining a relatively vulnerable node in the power distribution network, namely a target node, namely a node needing vulnerability optimization, and performing targeted vulnerability optimization on the target node; according to the technical scheme, the vulnerable nodes in the power distribution network can be effectively found, the accuracy is high, the reasonability is high, and the vulnerability of the power distribution network is pointedly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply optimization, and in particular to a vulnerability optimization method for power supply area nodes. Background Art

[0002] With the development of power systems and the improvement of their intelligence, distribution networks are playing an increasingly important role in energy transformation and smart city construction. However, the complexity and diversity of distribution networks lead to their high vulnerability. Once a failure or attack occurs, it may lead to the paralysis of the power system and affect the development of social economy. Therefore, how to evaluate and optimize the vulnerability of each node in the power supply area is a very important topic.

[0003] The existing methods for optimizing and reducing the vulnerability of power supply networks are: first, define an effective importance index, strengthen the protection of key nodes defined by the index, reduce the possibility of failure, and avoid excessive chain reactions. Second, add new lines to the power supply network to improve connectivity, thereby reducing the vulnerability of the power supply network; however, the existing methods for optimizing and reducing the vulnerability of power supply networks lack pertinence and have limited optimization effects. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the vulnerability of power supply area nodes, aiming to solve the problem that the existing methods for optimizing and reducing the vulnerability of power supply networks lack pertinence and have limited optimization effects.

[0005] The technical solution proposed by the present invention is:

[0006] A vulnerability optimization method for power supply area nodes, comprising:

[0007] Establishing a vulnerability assessment indicator based on complex network theory and a risk assessment indicator based on risk theory, wherein the vulnerability assessment indicator based on complex network theory and the risk assessment indicator based on risk theory are established independently and in parallel;

[0008] Through the hierarchical analysis method, the obtained node vulnerability indexes are weighted summed, and based on the summation result and the risk index of each node, the comprehensive evaluation index of the vulnerability of the distribution network nodes is determined;

[0009] Obtain the actual structural data and power flow calculation results of the distribution network to calculate the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes;

[0010] Based on the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes, relatively vulnerable nodes in the distribution network are determined and marked as target nodes;

[0011] The target node is optimized for vulnerability.

[0012] Preferably, the vulnerability assessment index based on complex network theory includes node degree, node betweenness and node injection power; the calculation formula of the node degree is:

[0013] The node degree calculation formula is as follows:

[0014]

[0015] Where, ND i is the node degree of node i; D i is the number of edges connected to node i; V ad are all nodes connected to node i; D 2 is the average degree of system nodes, D j is the number of edges connected to the jth node connected to the i-th node; the nodes include power supply points and load points; the edges include transmission lines and transformer branches;

[0016] The node betweenness calculation formula is as follows:

[0017]

[0018] In the formula, NB i is the node betweenness of node i; S b is the system baseline capacity; m is the generator node, G is the total number of generator nodes; n is the load node, L is the total number of load nodes; S m is the actual power of the generator node; S n is the actual power consumed by the load node; I mn (i, j) is the current generated on the ij branch after the unit current source is injected into the mn node; j is the node connected to the node i;

[0019] The calculation formula of the node injection power is as follows:

[0020]

[0021] In the formula, NP i The node injection power for node i; P i is the injected power of node i; S b This is the system baseline capacity.

[0022] Preferably, the risk assessment index based on risk theory includes a node voltage over-limit risk index; the calculation formula of the node voltage over-limit risk index is:

[0023]

[0024] Where NRU iis the node voltage over-limit risk indicator of node i; Ui represents the per unit value of bus i voltage.

[0025] Preferably, the node vulnerability indexes obtained by the hierarchical analysis method are weighted summed, and based on the summation result and the risk index of each node, a comprehensive evaluation index of the vulnerability of the distribution network node is determined, including:

[0026] The analytic hierarchy process is used to determine the weights of each vulnerability assessment indicator based on complex network theory. The vulnerability assessment indicators based on complex network theory are divided into different levels according to their mutual connections. Through the optimization and importance analysis of each level, quantitative calculation is performed to determine the first weight corresponding to the node degree, the second weight corresponding to the node betweenness, and the third weight corresponding to the node injection power.

[0027] The calculation formula for determining the comprehensive evaluation index of the vulnerability of the distribution network node based on the first weight, the second weight and the third weight is:

[0028] N i =(ω1ND i +ω2NB i +ω3NP i )×NRU i ,

[0029] Where NRU i is the comprehensive evaluation index of the vulnerability of distribution network node i; ω1 is the first weight; ω2 is the second weight; ω3 is the third weight.

[0030] Preferably, the obtaining of actual structural data of the distribution network and power flow calculation results to calculate the actual value of the comprehensive vulnerability evaluation index of the distribution network node includes:

[0031] Use forward-backward substitution method or improved algorithm for power flow calculation;

[0032] Based on the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes, relatively vulnerable nodes in the distribution network are determined and marked as target nodes, including:

[0033] determining a first preset value;

[0034] A distribution network node whose actual value of the comprehensive vulnerability evaluation index is less than a first preset value is determined as a relatively vulnerable node and marked as a target node.

[0035] Preferably, the vulnerability optimization of the target node includes:

[0036] Establish a simplified model of complex power network through the relationship between nodes and edges;

[0037] Attack the simplified topology model of the complex power network based on different attack modes to obtain the simplified model of the complex power network after destruction;

[0038] Based on the vulnerability reduction strategy, the simplified topology model of the complex power network is transformed to obtain a simplified model of the complex power network after transformation;

[0039] Attack the simplified model of the transformed complex power network based on different attack modes to obtain the destroyed simplified model of the transformed complex power network;

[0040] The vulnerability of the simplified model of the complex power network after destruction is calculated according to the network connectivity, as well as the vulnerability of the simplified model of the modified complex power network after destruction.

[0041] Preferably, the simplified model of the complex power network is an unweighted undirected graph obtained by simplifying the connection relationship between nodes and edges by ignoring the specific electrical parameters and physical constraints of the power network; the simplified topological model of the complex power network is transformed based on the vulnerability reduction strategy to obtain the transformed simplified model of the complex power network, including:

[0042] Based on the vulnerability reduction strategy, a maximum edge betweenness spanning tree with edge betweenness as weight is formed, and the adjacent Hub nodes in the maximum edge betweenness spanning tree are found in the neighbor relationship matrix and decoupled and reconnected to form a simplified model of the complex power network after the transformation, wherein the neighbor relationship matrix reflects the degree relationship between adjacent nodes in the complex power network, and the Hub node is a preset degree node in the complex power network.

[0043] Preferably, the vulnerability reduction strategy is based on forming a maximum edge betweenness spanning tree with edge betweenness as the weight, searching for adjacent Hub nodes in the maximum edge betweenness spanning tree in the neighbor relationship matrix and performing decoupling and reconnection to form a simplified model of the complex power network after transformation, including:

[0044] Assign the edge betweenness as a weight to each edge, and put the edge with the largest weight into the spanning tree set T;

[0045] Select the edge with the largest weight from the remaining edge set, put the edge with the largest weight into the spanning tree set T without forming a loop, then add the current edge to the spanning tree set T, and repeat the process until the spanning tree set T becomes the spanning tree with the maximum edge betweenness of the complex power network;

[0046] Record the edges that do not enter the spanning tree set T and put them into the set Q;

[0047] Improve the maximum edge betweenness spanning tree network using Hub node decoupling.

[0048] Preferably, the method of improving the maximum edge betweenness spanning tree network by using Hub node decoupling includes:

[0049] Use the neighbor relationship matrix to find the directly connected hub nodes in the maximum spanning tree set T, and disconnect the edges of the directly connected hub nodes i and j to achieve hub node decoupling;

[0050] Find the remaining adjacent nodes of hub nodes i and j, count them into sets I and J respectively, and confirm the degree of each node in sets I and J in the spanning tree set T and the degree in the complex power network;

[0051] Find the node with the smallest degree in the spanning tree set T in set I and set J respectively. When the nodes with the smallest degree in the spanning tree set T found in set I and set J are equal, select the node with the smaller degree in the complex power network and connect the current nodes to obtain the improved maximum spanning tree network T. n ;

[0052] The improved maximum generation network T n Add the set Q to get the improved complex network simplified model A n .

[0053] Through the above technical solution, the following beneficial effects can be achieved:

[0054] The vulnerability optimization method of power supply area nodes proposed by the present invention can optimize power supply nodes more targetedly. Thereby improving the optimization effect; this method is first based on the vulnerability assessment index of complex network theory and the risk assessment index based on risk theory; then, the obtained node vulnerability index is weighted and summed by the hierarchical analysis method, and based on the summation result and the risk index of each node, the vulnerability comprehensive evaluation index of the distribution network node is determined; then the actual value of the vulnerability comprehensive evaluation index of the distribution network node is calculated; then, based on the actual value of the vulnerability comprehensive evaluation index of the distribution network node, the relatively vulnerable nodes in the distribution network, that is, the target nodes, that is, the nodes that need to be optimized for vulnerability, are determined, and then the target nodes are optimized for vulnerability in a targeted manner; that is, this technical solution can effectively find vulnerable nodes in the distribution network with high accuracy and rationality, and then reduce the vulnerability of the distribution network in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0056] Figure 1This is a flowchart of a first embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0058] The present invention proposes a vulnerability optimization method for power supply area nodes.

[0059] As attached Figure 1 As shown, in a first embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, this embodiment includes the following steps:

[0060] Step S110: establishing a vulnerability assessment index based on complex network theory and a risk assessment index based on risk theory, wherein the vulnerability assessment index based on complex network theory and the risk assessment index based on risk theory are established independently and in parallel.

[0061] Step S120: Perform weighted summation on the obtained node vulnerability indicators through the hierarchical analysis method, and determine the comprehensive vulnerability evaluation index of the distribution network node based on the summation result and the risk index of each node.

[0062] Step S130: Acquire actual structural data of the distribution network and power flow calculation results to calculate actual values ​​of comprehensive vulnerability evaluation indicators of distribution network nodes.

[0063] Step S140: Determine relatively vulnerable nodes in the distribution network based on the actual values ​​of the comprehensive vulnerability evaluation indicators of the distribution network nodes, and mark them as target nodes.

[0064] Step S150: Optimize the vulnerability of the target node.

[0065] The vulnerability optimization method of power supply area nodes proposed by the present invention can optimize power supply nodes more targetedly. Thereby improving the optimization effect; this method is first based on the vulnerability assessment index of complex network theory and the risk assessment index based on risk theory; then, the obtained node vulnerability index is weighted and summed by the hierarchical analysis method, and based on the summation result and the risk index of each node, the vulnerability comprehensive evaluation index of the distribution network node is determined; then the actual value of the vulnerability comprehensive evaluation index of the distribution network node is calculated; then, based on the actual value of the vulnerability comprehensive evaluation index of the distribution network node, the relatively vulnerable nodes in the distribution network, that is, the target nodes, that is, the nodes that need to be optimized for vulnerability, are determined, and then the target nodes are optimized for vulnerability in a targeted manner; that is, this technical solution can effectively find vulnerable nodes in the distribution network with high accuracy and rationality, and then reduce the vulnerability of the distribution network in a targeted manner.

[0066] In a second embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the first embodiment, the vulnerability assessment index based on complex network theory includes node degree, node betweenness and node injection power; the calculation formula of the node degree is:

[0067] The node degree calculation formula is as follows:

[0068]

[0069] Where, ND i is the node degree of node i; D i is the number of edges connected to node i; V ad are all nodes connected to node i; D 2 is the average degree of system nodes, D j is the number of edges connected to the jth node connected to the i-th node; the nodes include power supply points and load points; the edges include transmission lines and transformer branches.

[0070] Specifically, considering that the distribution network is more sparse than the transmission network, the idea of ​​cohesion is integrated into the node degree, which not only considers the number of edges connected to the node, but also the influence of all nodes connected to the node.

[0071] The node betweenness calculation formula is as follows:

[0072]

[0073] In the formula, NB i is the node betweenness of node i; S b is the system baseline capacity; m is the generator node, G is the total number of generator nodes; n is the load node, L is the total number of load nodes; S m is the actual power of the generator node; S n is the actual power consumed by the load node; I mn (i, j) is the current generated in the ij branch after the unit current source is injected into the mn node; j is the node connected to the node i.

[0074] Specifically, parameters such as node capacity and branch reactance are taken into account in order to reflect the objective power flow distribution in the power system, that is, the utilization of each node by power transmission between generators and load nodes.

[0075] The calculation formula of the node injection power is as follows:

[0076]

[0077] In the formula, NP i The node injection power for node i; Pi is the injected power of node i; S b This is the system baseline capacity.

[0078] Specifically, in order to reflect the importance of nodes in power transmission and allocation, the concept of node injection power is introduced.

[0079] In a third embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the second embodiment, the risk assessment index based on risk theory includes a node voltage over-limit risk index; the calculation formula of the node voltage over-limit risk index is:

[0080]

[0081] Where NRU i is the node voltage over-limit risk indicator of node i; Ui represents the per unit value of bus i voltage.

[0082] In a fourth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the third embodiment, step S120 includes the following steps:

[0083] Step S410: A hierarchical analysis method is used to determine the weights of various vulnerability assessment indicators based on complex network theory. The vulnerability assessment indicators based on complex network theory are divided into different levels according to the mutual connections between them. Quantitative calculations are performed through optimization and importance analysis of each level to determine a first weight corresponding to the node degree, a second weight corresponding to the node betweenness, and a third weight corresponding to the node injection power.

[0084] Specifically, in this embodiment, the first weight corresponding to the node degree is 0.2; the second weight corresponding to the node betweenness is 0.2; and the third weight corresponding to the node injection power is 0.6.

[0085] Step S420: Determine a calculation formula for the comprehensive vulnerability evaluation index of the distribution network node based on the first weight, the second weight, and the third weight:

[0086] N i =(ω1ND i +ω2NB i +ω3NP i )×NRU i ,

[0087] Where NRU i is the comprehensive evaluation index of the vulnerability of distribution network node i; ω1 is the first weight; ω2 is the second weight; ω3 is the third weight.

[0088] Specifically, the calculation formula of the node comprehensive vulnerability index can be obtained as follows:

[0089] N i =(0.2×ND i +0.2×NB i +0.6×NP i )×NRU i .

[0090] In a fifth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the fourth embodiment, step S130 includes the following steps:

[0091] Step S510: Use the forward-backward method or the improved algorithm to perform power flow calculation.

[0092] Specifically, since the raw data required for each indicator is wide, including the voltage and power of each node, the simple network structure data cannot meet the requirements, and the distribution network needs to be calculated for the flow. Since the calculation network is a distribution network, the commonly used algorithms may not meet the accuracy requirements. Therefore, the forward-backward method or improved algorithm is used for flow calculation.

[0093] Step S140 includes the following steps:

[0094] Step S520: Determine a first preset value.

[0095] Step S530: determining a distribution network node whose actual value of the comprehensive vulnerability evaluation index is less than a first preset value as a relatively vulnerable node, and marking it as a target node.

[0096] Specifically, the larger the actual value of the comprehensive vulnerability evaluation index of each node in the distribution network, the higher the vulnerability level of the node in the distribution network, that is, the more fragile it is, and the more it needs to reduce its vulnerability; conversely, the smaller the comprehensive vulnerability index of each node, the lower the vulnerability level of the node in the distribution network, that is, the stronger it is, and the less it needs to reduce its vulnerability.

[0097] In a sixth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the fifth embodiment, step S150 includes the following steps:

[0098] Step S610: Establish a simplified model of a complex power network through the relationship between nodes and edges.

[0099] Step S620: attacking the complex power network simplified topology model based on different attack modes to obtain a damaged complex power network simplified model.

[0100] Step S630: transforming the simplified topology model of the complex power network based on the vulnerability reduction strategy to obtain a transformed simplified model of the complex power network.

[0101] Step S640: attacking the transformed simplified complex power network model based on different attack modes to obtain a destroyed transformed simplified complex power network model.

[0102] Step S650: Calculate the vulnerability of the simplified model of the damaged complex power network and the vulnerability of the simplified model of the modified complex power network after the damage according to the network connectivity.

[0103] Specifically, this embodiment provides a technical solution for optimizing the vulnerability of a target node.

[0104] In a seventh embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the sixth embodiment, the simplified model of the complex power network is an unweighted undirected graph obtained by simplifying the connection relationship between nodes and edges by ignoring specific electrical parameters and physical constraints of the power network; step S650 includes the following steps:

[0105] Step S710: Based on the vulnerability reduction strategy, a maximum edge betweenness spanning tree with edge betweenness as weight is formed, and adjacent Hub nodes in the maximum edge betweenness spanning tree are found in the neighbor relationship matrix and decoupled and reconnected to form a simplified model of the complex power network after the transformation, wherein the neighbor relationship matrix reflects the degree relationship between adjacent nodes in the complex power network, and the Hub node is a preset degree node in the complex power network.

[0106] In an eighth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the seventh embodiment, step S710 includes the following steps:

[0107] Step S810: assign the edge betweenness as a weight to each edge, and put the edge with the largest weight into the spanning tree set T.

[0108] Step S820: Select the edge with the largest weight from the remaining edge sets, and put the current edge with the largest weight into the spanning tree set T. If no loop is formed, then add the current edge to the spanning tree set T, and repeat the process until the spanning tree set T becomes the spanning tree with the maximum edge betweenness of the complex power network.

[0109] Step S830: Record the edges that have not entered the spanning tree set T and put them into the set Q.

[0110] Step S840: utilizing Hub node decoupling to improve the maximum edge betweenness spanning tree network.

[0111] In a ninth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the eighth embodiment, step S840 includes the following steps:

[0112] Step S910: Use the neighbor relationship matrix to find directly connected hub nodes in the maximum spanning tree set T, and disconnect the edges of the directly connected hub nodes i and j to achieve Hub node decoupling.

[0113] Step S920: Find the remaining adjacent nodes of the hub nodes i, j, count them into set I and set J respectively, and confirm the degree of each node in set I and set J in the spanning tree set T and the degree in the complex power network.

[0114] Step S930: Find the node with the smallest degree in the spanning tree set T in the set I and the set J respectively. When the nodes with the smallest degree in the spanning tree set T found in the set I and the set J are equal, select the node with the smaller degree in the complex power network, and connect the current node to obtain the improved maximum spanning tree network T. n .

[0115] Step S940: The improved maximum generation network T n Add the set Q to get the improved complex network simplified model A n .

[0116] In the tenth embodiment of a vulnerability optimization method for power supply area nodes proposed by the present invention, based on the ninth embodiment, the attack mode includes: destroying nodes according to different node importance indicators; the node importance indicator reflects the importance of destruction of each node or route in the entire power network system; when the node importance index value is larger, the node is more important, and the loss caused by destroying the current node is relatively larger; the attack mode includes: high degree node priority attack mode, high betweenness node priority attack mode, high node importance priority attack mode or high gravity node priority attack mode; the network connectivity refers to the survival rate of network nodes after being destroyed, and the network connectivity is the ratio of the number of nodes in the largest connected subgraph to the total number of nodes in the simplified topology model of the complex power network or the simplified topology model of the complex power network after transformation, and the formula is as follows:

[0117]

[0118] Where N sub Represents the number of nodes in the largest connected subgraph; N represents the total number of initial network nodes.

[0119] The high gravity node priority attack mode includes: calculating the high gravity indexes corresponding to all nodes or edges, and attacking from large to small according to the high gravity indexes until the number of attacks reaches a second preset value.

[0120] The calculation formula of the high gravity index is as follows:

[0121]

[0122] In the formula, g i is the high gravity index; V is the total set of nodes in the simplified model of complex power network, A represents the adjacency matrix of the simplified model of complex power network, k i and k j Represent the degrees of node i and node j respectively.

[0123] The above method can adopt a system including an evaluation module and an optimization module; the evaluation module (for example, a client with evaluation calculation) is used to establish a vulnerability assessment index based on complex network theory and a risk assessment index based on risk theory; the obtained node vulnerability index is weighted and summed through the hierarchical analysis method, and the comprehensive vulnerability evaluation index of the distribution network node is determined based on the summation result and the risk index of each node; the actual structural data of the distribution network and the flow calculation results are obtained to calculate the actual value of the comprehensive vulnerability evaluation index of the distribution network node; based on the actual value of the comprehensive vulnerability evaluation index of the distribution network node, relatively vulnerable nodes in the distribution network are determined and marked as target nodes; the optimization module (for example, a client with optimization calculation capability) is used to optimize the vulnerability of the target node.

[0124] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0125] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A vulnerability optimization method for power supply area nodes, characterized in that: include: Establishing a vulnerability assessment indicator based on complex network theory and a risk assessment indicator based on risk theory, wherein the vulnerability assessment indicator based on complex network theory and the risk assessment indicator based on risk theory are established independently and in parallel; Through the hierarchical analysis method, the obtained node vulnerability indexes are weighted summed, and based on the summation result and the risk index of each node, the comprehensive evaluation index of the vulnerability of the distribution network nodes is determined; Obtain the actual structural data and power flow calculation results of the distribution network to calculate the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes; Based on the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes, relatively vulnerable nodes in the distribution network are determined and marked as target nodes; The target node is optimized for vulnerability.

2. A vulnerability optimization method for power supply area nodes according to claim 1, characterized in that: The vulnerability assessment index based on complex network theory includes node degree, node betweenness and node injection power; the calculation formula of the node degree is: The node degree calculation formula is as follows: Where, ND i is the node degree of node i; D i is the number of edges connected to node i; V ad For all nodes connected to the i node; is the average degree of system nodes, D j is the number of edges connected to the jth node connected to the i-th node; the nodes include power supply points and load points; the edges include transmission lines and transformer branches; The node betweenness calculation formula is as follows: In the formula, NB i is the node betweenness of node i; S b is the system baseline capacity; m is the generator node, G is the total number of generator nodes; n is the load node, L is the total number of load nodes; S m is the actual power of the generator node; S n is the actual power consumed by the load node; I mn (i, j) is the current generated on the ij branch after the unit current source is injected into the mn node; j is the node connected to the node i; The calculation formula of the node injection power is as follows: In the formula, NP i The node injection power for node i; P i is the injected power of node i; S b This is the system baseline capacity.

3. The vulnerability optimization method of power supply area nodes according to claim 1 is characterized in that: The risk assessment index based on risk theory includes a node voltage over-limit risk index; the calculation formula of the node voltage over-limit risk index is: Where NRU i is the node voltage over-limit risk indicator of node i; Ui represents the per unit value of bus i voltage.

4. The vulnerability optimization method of power supply area nodes according to claim 1 is characterized in that: The node vulnerability indexes obtained by the hierarchical analysis method are weighted and summed, and based on the summation result and the risk index of each node, the comprehensive evaluation index of the vulnerability of the distribution network node is determined, including: The analytic hierarchy process is used to determine the weights of each vulnerability assessment indicator based on complex network theory. The vulnerability assessment indicators based on complex network theory are divided into different levels according to their mutual connections. Through the optimization and importance analysis of each level, quantitative calculation is performed to determine the first weight corresponding to the node degree, the second weight corresponding to the node betweenness, and the third weight corresponding to the node injection power. The calculation formula for determining the comprehensive evaluation index of the vulnerability of the distribution network node based on the first weight, the second weight and the third weight is: N i =(ω1ND i +ω2NB i +ω3NP i )×NRU i , Where NRU i is the comprehensive evaluation index of the vulnerability of distribution network node i; ω1 is the first weight; ω2 is the second weight; ω3 is the third weight.

5. The vulnerability optimization method of power supply area nodes according to claim 1 is characterized in that: The obtaining of actual structural data and power flow calculation results of the distribution network to calculate the actual value of the comprehensive vulnerability evaluation index of the distribution network node includes: Use forward-backward substitution method or improved algorithm for power flow calculation; Based on the actual value of the comprehensive vulnerability evaluation index of the distribution network nodes, relatively vulnerable nodes in the distribution network are determined and marked as target nodes, including: determining a first preset value; A distribution network node whose actual value of the comprehensive vulnerability evaluation index is less than a first preset value is determined as a relatively vulnerable node and marked as a target node.

6. The vulnerability optimization method of power supply area nodes according to claim 1, characterized in that: The vulnerability optimization of the target node includes: Establish a simplified model of complex power network through the relationship between nodes and edges; Attack the simplified topology model of the complex power network based on different attack modes to obtain the simplified model of the complex power network after destruction; Based on the vulnerability reduction strategy, the simplified topology model of the complex power network is transformed to obtain a simplified model of the complex power network after transformation; Attack the simplified model of the transformed complex power network based on different attack modes to obtain the destroyed simplified model of the transformed complex power network; The vulnerability of the simplified model of the complex power network after destruction is calculated according to the network connectivity, as well as the vulnerability of the simplified model of the modified complex power network after destruction.

7. A method for optimizing the vulnerability of power supply area nodes according to claim 6, characterized in that: The simplified model of the complex power network is an unweighted undirected graph obtained by simplifying the connection relationship between nodes and edges, ignoring the specific electrical parameters and physical constraints of the power network; The complex power network simplified topology model is transformed based on the vulnerability reduction strategy to obtain a transformed complex power network simplified model, including: Based on the vulnerability reduction strategy, a maximum edge betweenness spanning tree with edge betweenness as weight is formed, and the adjacent Hub nodes in the maximum edge betweenness spanning tree are found in the neighbor relationship matrix and decoupled and reconnected to form a simplified model of the complex power network after the transformation, wherein the neighbor relationship matrix reflects the degree relationship between adjacent nodes in the complex power network, and the Hub node is a preset degree node in the complex power network.

8. A method for optimizing the vulnerability of power supply area nodes according to claim 7, characterized in that: The vulnerability reduction strategy is based on forming a maximum edge betweenness spanning tree with edge betweenness as the weight, finding the adjacent Hub nodes in the maximum edge betweenness spanning tree in the neighbor relationship matrix and performing decoupling and reconnection to form a simplified model of the complex power network after transformation, including: Assign the edge betweenness as a weight to each edge, and put the edge with the largest weight into the spanning tree set T; Select the edge with the largest weight from the remaining edge set, put the edge with the largest weight into the spanning tree set T without forming a loop, then add the current edge to the spanning tree set T, and repeat the process until the spanning tree set T becomes the spanning tree with the maximum edge betweenness of the complex power network; Record the edges that do not enter the spanning tree set T and put them into the set Q; Improve the maximum edge betweenness spanning tree network using Hub node decoupling.

9. A method for optimizing the vulnerability of power supply area nodes according to claim 8, characterized in that: The method of improving the maximum edge betweenness spanning tree network by using Hub node decoupling includes: Use the neighbor relationship matrix to find the directly connected hub nodes in the maximum spanning tree set T, and disconnect the edges of the directly connected hub nodes i and j to achieve hub node decoupling; Find the remaining adjacent nodes of hub nodes i and j, count them into sets I and J respectively, and confirm the degree of each node in sets I and J in the spanning tree set T and the degree in the complex power network; Find the node with the smallest degree in the spanning tree set T in set I and set J respectively. When the nodes with the smallest degree in the spanning tree set T found in set I and set J are equal, select the node with the smaller degree in the complex power network and connect the current nodes to obtain the improved maximum spanning tree network T. n ; The improved maximum generation network T n Add the set Q to get the improved complex network simplified model A n .

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