Charging station access node selection method and system considering vulnerability of power distribution network

Abstract

The invention provides a charging station access node selection method and system considering the vulnerability of a power distribution network, and belongs to the field of charging station site selection. The method comprises the steps that a power distribution network topological structure of a power distribution network is acquired, and the power distribution network topological structure comprises a plurality of power distribution nodes; calculating a plurality of node vulnerability parameter sets of the plurality of power distribution network nodes according to the power distribution node vulnerability index set; based on a Jaya-AHP method and an entropy weight method, performing index weight fusion distribution on the power distribution node vulnerability index set to obtain a vulnerability index weight set; and according to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculating and screening to obtain an optimal access node, and performing charging station access. By accurately identifying the vulnerability difference of each node of the power distribution network, the centralized layout of the charging station in a node area with relatively high vulnerability is effectively avoided, and the access node with low vulnerability and proper geographic position is preferably selected, so that the impact of the charging load on the power grid is reduced.

Description

Technical Field

[0002] The present invention relates to the field of charging station location selection, and particularly to a method and system for selecting charging station access nodes considering the vulnerability of the distribution network. Background Art

[0003] With the rapid development of the new energy vehicle industry, the construction scale of electric vehicle charging stations has been continuously expanding, and the selection of nodes for connecting charging stations to the distribution network has become a key factor affecting the safe and stable operation of the power grid. The reasonable selection of charging station access nodes not only relates to the power supply reliability of charging facilities, but also directly affects the ability of the distribution network to withstand the impact of charging loads.

[0004] At present, it is difficult to accurately identify the vulnerability differences of each node in the distribution network in the method for selecting nodes for connecting electric vehicle charging stations to the distribution network. For example, Patent CN108683182B proposes a vulnerability assessment system based on static indicators such as voltage redundancy and load shedding rate, but this method does not consider indicators reflecting the dynamic characteristics of the power grid such as power flow distribution entropy and cohesion change rate, resulting in inaccurate and incomplete identification of node vulnerability differences. Patent CN104268336B uses Voronoi diagrams to divide the service range of charging stations, but does not combine with the characteristics of distribution network nodes and cannot effectively distinguish the vulnerability levels of each node.

[0005] Due to the lack of accurate identification of node vulnerability differences, the existing charging station location selection methods are likely to cause concentrated layout of charging stations in node areas with higher vulnerability. Patent CN102880921B conducts charging station location selection with the goals of economy and user convenience, but does not consider the vulnerability characteristics of nodes, which may lead to multiple charging stations accessing the same vulnerable area. Patent CN106803130B optimizes the coordinated planning of distributed power sources and the distribution network through genetic algorithms, but the weight allocation depends on fixed coefficients and fails to effectively avoid the problem of concentrated access to vulnerable nodes.

[0006] The above problems ultimately lead to a large impact of charging loads on the power grid. When charging stations are concentratedly connected to node areas with higher vulnerability, the access of large-scale charging loads will exacerbate the operation risks of these nodes, possibly causing problems such as voltage fluctuations and line overloads, threatening the safe and stable operation of the distribution network. Summary of the Invention

[0007] The present invention aims at the technical problem that in the prior art, when selecting nodes for connecting electric vehicle charging stations to the distribution network, the vulnerability differences of each node cannot be accurately identified, resulting in the easy concentration of charging stations in node areas with higher vulnerability, thereby causing a large impact of charging loads on the power grid, and provides a method and system for selecting charging station access nodes considering the vulnerability of the distribution network to solve this problem.

[0008] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a method for selecting a charging station access node considering the vulnerability of a distribution network, including: obtaining the distribution network topology of the distribution network, where the distribution network topology includes a plurality of distribution nodes; calculating a plurality of node vulnerability parameter sets of the plurality of distribution network nodes according to the distribution node vulnerability index set; based on the Jaya-AHP method and the entropy weight method, performing index weight fusion allocation on the distribution node vulnerability index set to obtain a vulnerability index weight set; calculating and screening to obtain an optimal access node according to the vulnerability index weight set and the plurality of node vulnerability parameter sets, and performing charging station access.

[0009] In a second aspect, the present invention provides a system for selecting a charging station access node considering the vulnerability of a distribution network, including: a topology acquisition module for obtaining the distribution network topology of the distribution network, where the distribution network topology includes a plurality of distribution nodes; a vulnerability calculation module for calculating a plurality of node vulnerability parameter sets of the plurality of distribution network nodes according to the distribution node vulnerability index set; a weight fusion module for performing index weight fusion allocation on the distribution node vulnerability index set based on the Jaya-AHP method and the entropy weight method to obtain a vulnerability index weight set; a node optimization module for calculating and screening to obtain an optimal access node according to the vulnerability index weight set and the plurality of node vulnerability parameter sets, and performing charging station access.

[0010] The beneficial effects of the present invention are as follows: Obtain the distribution network topology of the distribution network, where the distribution network topology includes a plurality of distribution nodes, thereby providing basic data support for subsequent node vulnerability analysis. Calculate a plurality of node vulnerability parameter sets of the plurality of distribution network nodes according to the distribution node vulnerability index set, thereby accurately quantifying the vulnerability levels of each node and effectively identifying the vulnerability differences among the nodes of the distribution network. Based on the Jaya-AHP method and the entropy weight method, perform index weight fusion allocation on the distribution node vulnerability index set to obtain a vulnerability index weight set, thereby overcoming the defects of strong subjectivity or insufficient objectivity of traditional weight allocation methods and realizing the fusion processing of subjective and objective weights. Calculate and screen to obtain an optimal access node according to the vulnerability index weight set and the plurality of node vulnerability parameter sets, and perform charging station access, thereby avoiding the concentrated layout of charging stations in node areas with higher vulnerability, selecting access nodes with low vulnerability and suitability, and effectively reducing the impact of charging load on the power grid.

[0011] Through the above technical solutions, the present application can accurately identify the vulnerability differences among the nodes of the distribution network, effectively avoid the concentrated layout of charging stations in node areas with higher vulnerability, select access nodes with low vulnerability and suitable geographical locations, thereby reducing the impact of charging load on the power grid. Description of the Drawings

[0012] Figure 1Schematic flow chart of the method for selecting charging station access nodes considering the vulnerability of the distribution network provided by the present invention; Figure 2 Schematic diagram of the distribution network topology of a certain distribution network provided by the present invention; Figure 3 Schematic diagram of the structure of the charging station access node selection system considering the vulnerability of the distribution network provided by the present invention.

[0013] In the drawings, the components represented by the reference numerals are as follows: Topology acquisition module 11, vulnerability calculation module 12, weight fusion module 13, node optimization module 14. Detailed implementation manners

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0015] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0016] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be practiced without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0017] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a method for selecting charging station access nodes considering the vulnerability of the distribution network, including: S1. Obtain the distribution network topology of the distribution network, where the distribution network topology includes a plurality of distribution nodes.

[0018] Specifically, the distribution network topology refers to the abstract representation of the connection relationship and spatial layout among various electrical equipment and lines in the distribution network. The distribution network topology includes multiple distribution nodes, which are key connection points in the distribution network, including but not limited to substation outgoing nodes, branch nodes, load nodes, switch nodes, etc. Each distribution node represents a location point in the distribution network with specific electrical characteristics and functions.

[0019] Obtaining the distribution network topology provides a basis for subsequent vulnerability analysis and optimal selection of charging station access nodes. By comprehensively grasping the distribution network topology, it can provide a basis for the reasonable access of charging stations to ensure that it will not have an adverse impact on the safe and stable operation of the distribution network after access.

[0020] S2. Calculate multiple node vulnerability parameter sets of multiple distribution network nodes according to the distribution node vulnerability index set.

[0021] Specifically, first, identify all distribution nodes in the distribution network topology, including but not limited to key power facility nodes such as substation nodes, switch nodes, branch nodes, load nodes, etc. Second, for each distribution node, calculate the corresponding vulnerability parameters according to the evaluation indexes in the distribution node vulnerability index set. Among them, the vulnerability index set can include multi-dimensional evaluation indexes such as node connectivity, load importance, fault propagation influence range, equipment aging degree, geographical location risk coefficient, historical fault frequency, etc.

[0022] Then, use the comprehensive evaluation method to weight each vulnerability index. By assigning corresponding weight coefficients to different indexes and comprehensively considering the influence degree of each factor on node vulnerability, the vulnerability parameter value of each node is obtained. After that, the vulnerability parameters of all nodes are uniformly standardized to form a complete set of multiple node vulnerability parameter sets of multiple distribution network nodes, providing a quantitative basis for subsequent risk assessment and decision optimization.

[0023] By calculating multiple node vulnerability parameter sets of multiple distribution network nodes, it is possible to achieve an accurate quantitative assessment of the vulnerability of multiple nodes in the distribution network, comprehensively reflect the weakness of each node in the distribution network, and provide technical support for improving the reliability and security of the distribution network operation.

[0024] S3. Based on the Jaya-AHP method and the entropy weight method, fuse and allocate the weights of the distribution node vulnerability index set to obtain the vulnerability index weight set.

[0025] Specifically, first, the AHP method is used to calculate the subjective weights of the distribution node vulnerability index set. The AHP method decomposes the vulnerability evaluation problem into an objective layer, a criterion layer, and an index layer by constructing a hierarchical structure model. It establishes a judgment matrix through pairwise comparison, determines the relative importance of each index based on expert experience and engineering practice, and calculates the subjective weight vector. Secondly, the Jaya optimization algorithm is introduced to improve and optimize the traditional AHP method. Based on the optimization principle of approaching the optimal solution and moving away from the worst solution, the Jaya algorithm iteratively optimizes the judgment matrix in the AHP method to effectively reduce the problem of inconsistent subjective judgments and obtain the improved Jaya-AHP subjective weights.

[0026] Meanwhile, the entropy weight method is used to calculate the objective weights of the vulnerability index set. Based on the information entropy theory, the entropy weight method objectively determines the weight coefficients according to the dispersion degree and information content of each index data. The greater the amount of information and the higher the degree of variation of an index, the greater the weight value it obtains.

[0027] After that, the subjective weights obtained by the Jaya-AHP method and the objective weights obtained by the entropy weight method are fused. The weighted combination method is used to synthesize the two weight information, forming a vulnerability index weight set that combines subjective experience and objective data characteristics.

[0028] By obtaining the vulnerability index weight set, the respective advantages of subjective weights and objective weights can be fully utilized, realizing the reasonable allocation of vulnerability index weights, improving the accuracy and reliability of the weight determination results, and providing a reliable weight basis for subsequent node vulnerability evaluation.

[0029] S4. According to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculate and screen to obtain the optimal access node and conduct the charging station access.

[0030] Specifically, first, the vulnerability index weight set is weighted with each node vulnerability parameter set, and the weighted comprehensive evaluation method is used to comprehensively evaluate the vulnerability of each distribution node. Through the multiplication and summation operation of weights and parameters, the comprehensive vulnerability value of each distribution node is obtained. Secondly, a node optimization criterion is established, and the node screening is carried out with the minimum vulnerability value as the optimization goal. The smaller the vulnerability value of a node, the lower its weakness degree in the distribution network, and the stronger its bearing capacity and better access conditions. Then, the comprehensive vulnerability values of all candidate nodes are sorted and compared, and the node with the smallest vulnerability value is selected as the optimal access node. After determining the optimal access node, formulate the corresponding charging station access plan, including technical parameters such as access mode, capacity configuration, and protection configuration, to complete the grid access work of the charging station.

[0031] Through the above process, the optimal access location of the charging station can be determined based on the quantified vulnerability evaluation results, effectively avoiding the concentrated layout of charging stations in areas with high-vulnerability nodes, avoiding the adverse effects of accessing weak nodes on the stability and reliability of the distribution system, improving the scientificity and rationality of the charging station access decision-making, thereby reducing the impact of the charging load on the power grid and ensuring the safe and stable operation of the distribution network.

[0032] Further, obtain the distribution network topology structure of the distribution network, including: S11. Obtain all multiple distribution nodes in the distribution network and divide them into multiple power supply units, where each power supply unit includes multiple distribution nodes; S12. Construct the mapping relationship between multiple power supply units and multiple charging stations for which access nodes are to be selected to obtain the distribution network topology structure.

[0033] In a feasible implementation manner, first, obtain all distribution node information in the distribution network, including basic data such as the location coordinates, equipment capacity, and electrical connection relationships of various power facility nodes such as substation nodes, switch station nodes, branch nodes, and load nodes. According to the power supply scope, electrical topology structure, and operation and management requirements of the distribution network, group and divide all distribution nodes according to the power supply area and electrical characteristics to form multiple power supply units. Among them, each power supply unit includes multiple distribution nodes, and these nodes are interconnected through main lines and branch lines to form a relatively independent power supply network electrically and jointly undertake the power supply tasks of a specific area. The division of power supply units comprehensively considers factors such as the power supply radius of substations, load distribution density, and geographical proximity.

[0034] Then, identify the attribute information such as the geographical coordinates, power demand, and service scope of multiple charging stations for which access nodes are to be selected. According to the spatial distribution characteristics and power supply requirements of the charging stations, analyze their spatial proximity relationship and power supply accessibility to each power supply unit. Subsequently, construct the mapping relationship between multiple power supply units and the charging stations to be accessed, and establish the corresponding relationship from the power supply unit to the charging station. This mapping relationship follows the principle of proximity by distance and the principle of matching power supply capabilities to determine the candidate power supply unit range to which each charging station belongs and obtain the complete distribution network topology structure.

[0035] Through the above steps, the distribution network topology structure can be systematically constructed, the power supply unit division and the charging station mapping relationship can be clarified, providing an accurate network basis for subsequent node vulnerability analysis and improving the pertinence and scientificity of access node selection.

[0036] Further, according to the distribution node vulnerability index set, calculate multiple node vulnerability parameter sets of multiple distribution network nodes, including: S21. Obtain the vulnerability index set of distribution nodes, where the vulnerability index set of distribution nodes includes the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy; S22. Calculate the cohesion change rate of multiple distribution nodes; S23. Calculate the power flow distribution entropy of multiple distribution nodes; S24. Calculate the voltage growth entropy of multiple distribution nodes; S25. Integrate the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy of multiple distribution nodes to obtain multiple node vulnerability parameter sets.

[0037] In a preferred embodiment, first, establish a vulnerability index set of distribution nodes, which includes three core evaluation indexes: the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy. Among them, the cohesion change rate reflects the importance and connection tightness of the node in the network topology; the power flow distribution entropy characterizes the uniformity and stability of the power flow distribution at the node; the voltage growth entropy describes the regularity and predictability of the node voltage change.

[0038] Then, for each distribution node in the distribution network, based on the network topology and node connection relationship, calculate the cohesion change rate of each node. The cohesion change rate quantifies the importance of the node in maintaining network connectivity by analyzing the change amplitude of the network cohesion degree before and after the node removal. The larger the cohesion change rate, the more significant the impact of the node on the network structure. At the same time, based on the power flow calculation results of the distribution network, obtain the active power and reactive power distribution data of each distribution node. Using the information entropy theory, calculate the entropy value of the power flow distribution at each node to quantify the uncertainty and complexity of the power flow distribution. The larger the power flow distribution entropy value, the more uneven the power flow distribution at the node and the more unstable the operating state. In addition, obtain the voltage change data of each distribution node under different operating conditions and analyze the time series characteristics of the voltage fluctuation. Using the entropy value calculation method, quantify the randomness and irregularity degree of the voltage change of each node to obtain the voltage growth entropy value. The higher the voltage growth entropy value, the more irregular the voltage change of the node and the worse the voltage stability. Subsequently, integrate the calculated cohesion change rate, power flow distribution entropy, and voltage growth entropy of each distribution node to form a node vulnerability parameter matrix for each distribution node, providing a data basis for subsequent weight fusion and comprehensive evaluation.

[0039] Through the above technical solutions, it is possible to comprehensively quantify the vulnerability characteristics of each distribution node from three dimensions of network topology, power flow distribution, and voltage stability, construct a vulnerability evaluation index system, and provide reliable data support for the optimal selection of charging station access nodes.

[0040] Furthermore, calculating the cohesion change rate of multiple distribution nodes includes: S221. Define the distribution network as a weighted directed graph \(G\) with \(n\) nodes and \(m\) edges. The adjacency matrix of the weighted directed graph \(G\) is as follows: ; \(HH\) is the adjacency matrix. If there is a direct line connection between distribution node \(i\) and distribution node \(j\), then is assigned a value of 1, otherwise 0; S222. Calculate the network cohesion of the weighted directed graph \(G\) as follows: ; where represents the shortest electrical distance between node \(i\) and node \(j\), is the set of nodes of the weighted directed graph \(G\); S223. Calculate the cohesion change rate of multiple distribution nodes according to the network cohesion as follows: ; where is the cohesion change rate of the \(i\)-th distribution node, represents the subgraph formed after the weighted directed graph \(G\) is contracted through distribution node \(i\); is the cohesion of the subgraph.

[0041] In a preferred embodiment, first, model the distribution network as a weighted directed graph \(G\) with \(n\) nodes and \(m\) edges, where \(n\) represents the total number of distribution nodes in the distribution network, including substation nodes, switch station nodes, branch nodes, etc.; \(m\) represents the total number of power line connections in the distribution network, reflecting the electrical connection relationship between nodes. The adjacency matrix of the weighted directed graph \(G\) is as follows: ; This adjacency matrix \(HH\) is an \(n\times n\) square matrix used to describe the topological connection structure of the distribution network. For the element in the adjacency matrix, when there is a direct power line connection between distribution node \(i\) and distribution node \(j\), , indicating that electric energy can be directly transmitted between the two nodes; when there is no direct line connection between distribution node \(i\) and distribution node \(j\), 0, indicating that electric energy cannot be directly exchanged between the two nodes. Through this adjacency matrix, the physical connection topology of the distribution network can be completely characterized, laying a foundation for subsequent network analysis.

[0042] Then, based on the constructed weighted directed graph \(G\), calculate the network cohesion of the entire distribution network. The specific calculation formula is: ; This network cohesion A comprehensive index used to measure the connectivity tightness between nodes in a distribution network. The larger the value, the stronger the overall connectivity of the network. Among them, represents the shortest electrical distance between node i and node j, that is, the minimum number of line connections passed from node i to node j, which is calculated by the shortest path algorithm in graph theory (such as Dijkstra algorithm), reflecting the shortest path length of power transmission between two nodes; is the set of nodes of the weighted directed graph G, including all n distribution nodes in the distribution network; n(n - 1) in the denominator represents the total number of node pairs between any two different nodes in the distribution network, which is used to standardize the cohesion. The whole formula quantifies the overall connectivity tightness of the network by calculating the average of the sum of the reciprocals of the shortest distances between all node pairs.

[0043] Subsequently, according to the obtained network cohesion, the cohesion change rate of each node in the distribution network is calculated respectively . The specific calculation formula of this cohesion change rate is: ; Among them, is the cohesion change rate of the i-th distribution node, represents the subgraph formed after the contraction of the weighted directed graph G through the i-th distribution node; is the cohesion of the subgraph.

[0044] This cohesion change rate is used to quantify the contribution degree or influence degree of a single node to the connectivity of the entire distribution network. Among them, is the cohesion change rate of the i-th distribution node. The larger the value, the higher the importance of the node in maintaining the overall connectivity of the network, that is, the node is a key node of the network; is the network cohesion of the complete distribution network; represents the subgraph formed after removing the i-th distribution node, that is, the remaining network topology obtained by deleting the node i and all its related power line connections from the original weighted directed graph G; is the subgraph 's network cohesion, reflecting the connectivity tightness of the remaining network after removing node i; the whole ratio reflects the promotion effect of the existence of node i on the network cohesion. The larger the ratio, the greater the contribution of the node to the network connectivity and the more important its position in the network. From the perspective of power system operation, nodes with high cohesion change rate are usually key nodes or hub nodes of the network, and their faults or withdrawals will significantly affect the power supply connectivity and reliability of the distribution network.

[0045] Through the above steps, it is possible to quantify the importance and criticality of each distribution node in the network topology based on graph theory and network analysis theory, providing reliable topological indicators for the vulnerability assessment of the distribution network and effectively supporting the optimal selection of the charging station access nodes.

[0046] Further, calculate the power flow distribution entropy of multiple distribution nodes, including: S231. Calculate the active power change amount of multiple nodes when the unit load changes, as shown in the following formula: ; where, is the active power change amount of the i-th distribution node, is the active power after the unit load change of the i-th distribution node, is the initial active power of the i-th distribution node; S232. According to the active power change amount, calculate the total power flow impact of the whole network caused by the unit load change, as shown in the following formula: ; where, is the total power flow impact of the whole network caused by the unit load change of the i-th distribution node, is the total number of lines; S233. Calculate the power flow transfer impact ratio generated by the unit load change of the distribution node on the line, as shown in the following formula: ; where, is the power flow transfer impact ratio generated by the unit load change of the i-th distribution node on the k-th line; S234. According to the power flow transfer impact ratio, calculate the power flow distribution entropy, as shown in the following formula: ; where, is the power flow distribution entropy of the distribution network generated by the unit load change of the i-th distribution node.

[0047] In a preferred embodiment, first, for each node in the distribution network, calculate its active power change amount when the unit load changes. The calculation formula for the active power change amount is: ; is the active power change amount of the i-th distribution node, with the unit of MW or kW, indicating the change amplitude of the power output of the node under the load disturbance; is the active power after the unit load change of the i-th distribution node, that is, the actual power output value after the disturbance; [[ID=5⑦]] $P_{i0}$ is the initial active power of the $i$-th distribution node, that is, the reference power output value before the disturbance. By calculating the change in active power of multiple nodes when the unit load changes, the direct influence degree of the load change of a single node on its own power output is quantified.

[0048] Then, according to the change in active power of each node, calculate the total power flow impact in the whole network caused by the unit load change. The formula for calculating the total power flow impact in the whole network is: ; Among them, $\Delta P_{i}$ is the total power flow impact in the whole network caused by the unit load change of the $i$-th distribution node, reflecting the overall influence degree of the disturbance of this node on the power flow distribution of the entire distribution network; $L$ is the total number of lines in the distribution network, including the main lines and branch lines; $\Delta P_{ik}$ is the change in active power generated due to the load change of the $i$-th node. By accumulating the power changes of all lines, the power flow impact intensity generated by the disturbance of a single node in the whole network is quantified.

[0049] Subsequently, calculate the power flow transfer impact ratio generated by each line due to the unit load change of the distribution node. The formula for calculating the power flow transfer impact ratio is: ; Among them, $R_{ik}$ is the power flow transfer impact ratio generated by the $k$-th line when the unit load of the $i$-th distribution node changes. It is a dimensionless value, indicating the proportion of the impact intensity borne by this line in the whole network impact. This power flow transfer impact ratio reflects the distribution uniformity of the power flow impact on each line. The larger the ratio, the more concentrated the impact borne by this line.

[0050] Next, according to the power flow transfer impact ratio, use the information entropy theory to calculate the power flow distribution entropy. The formula for calculating the power flow distribution entropy is: ; Among them, $H_{i}$ is the power flow distribution entropy of the distribution network generated by the unit load change of the $i$-th distribution node, with the unit of nats or bits. The larger the value, the more dispersed and uneven the power flow distribution. $m$ is the number of lines participating in the calculation; $\ln$ is the natural logarithm function. This power flow distribution entropy quantifies the distribution uniformity of the power flow impact in the distribution network: a smaller entropy value indicates that the power flow impact is concentrated on a few lines, and the network operation state is relatively stable; a larger entropy value indicates that the power flow impact is dispersed on multiple lines, the uncertainty of the distribution network operation is higher, and the impact of this node on network stability is greater.

[0051] Through the above steps, the influence degree of the load change of each distribution node on the whole network power flow distribution can be scientifically quantified based on the information entropy theory, providing a reliable mathematical index for evaluating the power flow disturbance sensitivity of nodes, and effectively supporting the vulnerability analysis of the distribution network and the optimal selection of the charging station access nodes.

[0052] Furthermore, calculate the voltage growth entropy of multiple distribution nodes, including: S241. Obtain the voltage amplitude fluctuation of the distribution node after being disturbed, as shown in the following formula: ; In the formula, is the voltage amplitude of the j-th distribution node in the initial state; is the voltage amplitude of the j-th distribution node when it is disturbed; S242. Calculate the voltage amplitude change rate of the distribution node according to the voltage amplitude fluctuation, as shown in the following formula: ; where is the rated voltage of the j-th distribution node; S243. Calculate the voltage growth entropy according to the voltage amplitude change rate, as shown in the following formula: ; where is the voltage growth entropy of the j-th distribution node.

[0053] In a preferred embodiment, first, for each node in the distribution network, obtain the voltage amplitude fluctuation after it is disturbed. The calculation formula of the voltage amplitude fluctuation is: ; where is the voltage amplitude of the j-th distribution node in the initial state; is the voltage amplitude of the j-th distribution node when it is disturbed; is the voltage amplitude fluctuation, with the unit of kV or V, representing the absolute value of the voltage change of the j-th distribution node when it is affected by the disturbance of the i-th node; is the voltage amplitude of the j-th distribution node in the initial state, that is, the reference voltage value of this node during normal operation; is the voltage amplitude of the j-th distribution node when it is disturbed, that is, the actual voltage value of the j-th node after the load change and other disturbances occur at the i-th node. By obtaining the voltage amplitude fluctuation of the distribution node after being disturbed, the direct influence degree of the network disturbance on the voltage stability of each node is quantified.

[0054] Then, according to the voltage amplitude fluctuation, calculate the voltage amplitude change rate of each distribution node. The calculation formula for the voltage amplitude change rate is: ; Where, is the rated voltage of the jth distribution node; is the voltage amplitude change rate of the jth distribution node, a dimensionless value, representing the proportion of the voltage fluctuation of this node relative to its rated voltage; is the voltage amplitude fluctuation; is the rated voltage of the jth distribution node, that is, the standard voltage level when this node is designed to operate, such as 10 kV, 35 kV, etc. The voltage amplitude change rate is standardized to eliminate the dimensional difference between nodes of different voltage levels, facilitating unified vulnerability assessment. The larger the change rate, the worse the voltage stability of this node and the more sensitive it is to disturbances.

[0055] Subsequently, according to the voltage amplitude change rate, use the information entropy theory to calculate the voltage growth entropy. The calculation formula for the voltage growth entropy is: ; Where, is the voltage growth entropy of the jth distribution node, with the unit of nats or bits. The larger the value, the more irregular and unstable the voltage change of this node; n is the total number of nodes participating in the calculation in the distribution network; ln is the natural logarithm function; is the voltage amplitude change rate of the jth distribution node. Through the voltage growth entropy, the randomness and uncertainty degree of the node voltage change are quantified: a smaller entropy value indicates that the node voltage change is relatively regular and stable, and the voltage control ability for this node is stronger; a larger entropy value indicates that the node voltage change is more random and unpredictable, and this node has a higher vulnerability in terms of voltage stability and is prone to voltage fluctuations or instability phenomena under system disturbances.

[0056] Through the above steps, it is possible to scientifically quantify the voltage stability and disturbance resistance of each distribution node based on the information entropy theory, providing a reliable mathematical index for evaluating the voltage vulnerability of the nodes, effectively supporting the vulnerability analysis of the distribution network and the optimal selection of the charging station access nodes, and ensuring that the voltage stability of the distribution network will not be adversely affected after the charging station is connected.

[0057] Furthermore, based on the Jaya - AHP method and the entropy weight method, perform index weight fusion and allocation on the distribution node vulnerability index set to obtain the vulnerability index weight set, including: S31. Based on the Jaya - AHP method, process to obtain the optimized subjective weight column vector; S32. Based on the entropy weight method, process to obtain multiple objective weights of multiple distribution node vulnerability indicators in the distribution node vulnerability indicator set; S33. According to the optimized subjective weight column vector and multiple objective weights, calculate to obtain multiple comprehensive weights, and obtain the vulnerability indicator weight set, as shown in the following formula: ; Among them, is the comprehensive weight of the weight of the i-th vulnerability indicator, is the subjective weight of the weight of the i-th vulnerability indicator, is the objective weight of the weight of the i-th vulnerability indicator.

[0058] In a preferred embodiment, first, use the Jaya-AHP method to process and obtain the optimized subjective weight column vector. This method first constructs a hierarchical structure model of distribution node vulnerability indicators using the traditional AHP method, establishes a pairwise comparison matrix through expert judgment, and calculates the initial subjective weights. Then, introduce the Jaya optimization algorithm to improve the AHP method, utilize the optimization mechanism of the Jaya algorithm to tend to the optimal solution and stay away from the worst solution, automatically adjust the consistency of the judgment matrix through the iterative optimization process, reduce the randomness and inconsistency problems of subjective judgment, and finally obtain the optimized subjective weight column vector, thus obtaining the optimized subjective weight column vector.

[0059] At the same time, use the entropy weight method to process and obtain multiple objective weights of multiple distribution node vulnerability indicators in the distribution node vulnerability indicator set. The entropy weight method is based on the information entropy theory, and objectively determines the weight coefficient according to the dispersion degree and information content of the data of each vulnerability indicator. Specifically, analyze the data distribution characteristics of three vulnerability indicators, namely the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy, calculate the information entropy value of each indicator, and the indicator with greater information volume and higher variation degree obtains a greater objective weight, so as to obtain multiple objective weights of multiple distribution node vulnerability indicators.

[0060] Subsequently, according to the optimized subjective weight column vector and multiple objective weights, calculate to obtain multiple comprehensive weights, and form the vulnerability indicator weight set. The comprehensive weight calculation formula is: ; Among them, is the comprehensive weight of the weight of the i-th vulnerability indicator, which integrates subjective experience judgment and objective data characteristics, and has higher scientificity and rationality; is the subjective weight of the weight of the i-th vulnerability indicator, which reflects the understanding of the importance of indicators by expert experience and engineering practice; is the objective weight of the weight of the i-th vulnerability indicator, reflecting the information content and variation characteristics of the indicator data itself. m is the total number of vulnerability indicators. In this embodiment, m = 3, including the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy. This comprehensive weight calculation method gives full play to the respective advantages of subjective weights and objective weights, taking into account both the expert's experience judgment and the objective characteristics of the data, and realizing the scientificity and rationality of weight determination.

[0061] Through the above steps, it is possible to effectively combine the advantages of subjective experience and objective data, realize the reasonable distribution of the weights of vulnerability indicators, avoid the limitations of a single weight determination method, improve the accuracy and credibility of the vulnerability assessment of distribution network nodes, and provide a reliable weight basis for the subsequent selection of optimal access nodes.

[0062] Furthermore, based on the Jaya-AHP method, an optimized subjective weight column vector is obtained through processing, including: S311. Based on the Jaya-AHP method, a subjective weight objective function is constructed as follows: ; where β is the subjective weight column vector with a dimension of 1×m, and m is the number of indicators of multiple distribution node vulnerability indicators. is the judgment matrix of the i-th row and j-th column element; S312. According to the subjective weight objective function, an optimized subjective weight column vector is obtained through optimization processing.

[0063] In a preferred embodiment, first, based on the Jaya-AHP method, a subjective weight objective function is constructed. The expression of the subjective weight objective function is:

[0064] where minF( ) is the subjective weight objective function, which is used to measure the consistency degree of the judgment matrix. The smaller the function value, the better the consistency of weight distribution; is the subjective weight column vector with a dimension of 1×m, which contains the subjective weight coefficients of each vulnerability indicator; m is the number of indicators of multiple distribution node vulnerability indicators. In this embodiment, m = 3, corresponding to the three indicators of the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy respectively; is the judgment matrix of the i-th row and j-th column element, indicating the i-th vulnerability indicator is the subjective weight coefficient of the j-th vulnerability index; n is the number of rows of the judgment matrix, which is equal to the number of indicators m. This subjective weight objective function seeks the optimal weight allocation scheme by minimizing the deviation between each element of the judgment matrix and the weight ratio, ensuring that the judgment matrix has good consistency and reducing the logical contradictions and inconsistencies in subjective judgments.

[0065] Subsequently, according to the subjective weight objective function, the Jaya optimization algorithm is used for optimization to obtain the optimized subjective weight column vector. The Jaya algorithm is based on the optimization strategy of approaching the optimal solution and moving away from the worst solution. It continuously updates the weight vector through the iterative process to gradually reduce the value of the objective function. During the optimization process, the Jaya algorithm will automatically search for the weight combination that makes the judgment matrix have the best consistency, avoiding the cumbersome process of having to readjust the judgment matrix when the consistency test fails in the traditional AHP method. The optimization algorithm takes into account the constraint conditions of the weight vector, including: the non-negativity constraint of the weight, that is, all weight coefficients must be non-negative numbers; the normalization constraint of the weight, that is, the sum of all weight coefficients is equal to 1. Through the global optimization of the Jaya algorithm, an optimized subjective weight column vector that not only meets the expert's judgment intention but also has good mathematical consistency is finally obtained, providing a reliable subjective weight basis for subsequent weight fusion.

[0066] Through the above steps, the problem of difficult consistency test of the judgment matrix in the traditional AHP method can be effectively solved. By using the Jaya algorithm to automatically adjust and optimize the weight allocation, the scientificity and reliability of determining the subjective weight are improved, laying a solid foundation for the accurate allocation of the vulnerability index weight.

[0067] Furthermore, according to the vulnerability index weight set and the multiple node vulnerability parameter sets, the optimal access nodes are calculated and screened for the charging station access, including: S41. According to the vulnerability index weight set, perform weighted calculation on multiple node vulnerability parameters in the node vulnerability parameter set of each distribution node to obtain multiple comprehensive vulnerability parameters of multiple distribution nodes; S42. According to multiple comprehensive vulnerability parameters, screen and obtain multiple optimal access nodes in the power supply unit of each charging station for access.

[0068] In a preferred embodiment, first, according to the vulnerability index weight set, multiple node vulnerability parameters in the node vulnerability parameter set of each distribution node are weighted and calculated to obtain multiple comprehensive vulnerability parameters of multiple distribution nodes. Specifically, each weight coefficient in the obtained vulnerability index weight set is subjected to a weighted summation operation with the three vulnerability parameters of the cohesion change rate, power flow distribution entropy, and voltage growth entropy of each calculated distribution node. Through weighted calculation, the vulnerability indicators in three different dimensions are synthesized into a unified evaluation parameter, obtaining multiple comprehensive vulnerability parameters of multiple distribution nodes, and realizing a comprehensive quantitative evaluation of node vulnerability. The smaller the value of the comprehensive vulnerability parameter, the lower the vulnerability of the node, the better the comprehensive performance in terms of network topology, power flow distribution, and voltage stability, and the more suitable it is as a charging station access node.

[0069] Then, according to multiple comprehensive vulnerability parameters, multiple optimal access nodes are screened and obtained within the power supply unit of each charging station for access. First, according to the established mapping relationship between the power supply unit and the charging station, the range of candidate access nodes corresponding to each charging station is determined to ensure that the charging station selects an access node within its corresponding power supply unit. Secondly, within each power supply unit, the comprehensive vulnerability parameters of all candidate distribution nodes are sorted and compared, arranged in ascending order according to the vulnerability parameter values, and a node vulnerability ranking table is established. Subsequently, the node with the smallest comprehensive vulnerability parameter is selected as the optimal access node for the charging station. The smaller the vulnerability parameter, the smaller the negative impact on the operation stability of the distribution network after the charging station is accessed, and the better the safety and reliable operation of the distribution system can be guaranteed. After the selection of the optimal access node is completed, a corresponding charging station access plan is formulated, including technical measures such as access mode design, protection configuration, capacity matching, and line transformation, to realize the safe and reliable access of the charging station.

[0070] Through the above steps, the optimal access location of the charging station can be determined based on the scientific comprehensive evaluation result of vulnerability, effectively avoiding the adverse effects of accessing weak nodes on the stability and reliability of the distribution system, improving the scientificity and rationality of the charging station access decision-making, and ensuring the safe and stable operation of the distribution network.

[0071] As Figure 2 shown, a certain actual distribution network is used as the research object. The distribution network planning includes 54 distribution nodes, including 4 substations and 50 load nodes, forming a complete distribution network topology structure. The currently built substations S1, S4, S3 and distribution nodes of this distribution network Among them, the 28 existing nodes in the system include 25 load nodes and 3 substation nodes, and the remaining 26 nodes are nodes to be expanded. A certain distribution network includes the existing substations S1, S4, S2 and the distribution nodes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 21, 22, 30, 31, 33, 43, 44, 45. Taking substation S1 as the core, the first power supply unit is formed, including distribution nodes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 31, 33; taking substation S4 as the core, the second power supply unit is formed, including distribution nodes 18, 21, 22, 30; taking substation S3 as the core, the third power supply unit is formed, including distribution nodes 11, 12, 13, 14, 15, 16, 43, 44, 45. Subsequently, according to their geographical location distribution, the mapping relationships between the to-be-connected charging stations C1 - C10 and each power supply unit are established: the first power supply unit corresponds to charging stations C1, C4, C9, C10; the second power supply unit corresponds to charging stations C2, C7; the third power supply unit corresponds to charging stations C3, C5, C6, C8.

[0072] According to the technical solution proposed in this embodiment, a vulnerability assessment is carried out on the 28 existing nodes (25 load nodes + 3 substations) in the distribution network. First, calculate three vulnerability indicators of the cohesion change rate, power flow distribution entropy, and voltage growth entropy of each node; then use the Jaya - AHP method and the entropy weight method for weight fusion and distribution; finally, calculate the comprehensive vulnerability parameters of each node. The comprehensive vulnerability parameters of each node are shown in Table 1, which represents the results of the vulnerability indicators of the 28 - node distribution network. It can be seen from the evaluation results in Table 1 that: the distribution nodes 51, 52, 54 have not been built yet, and their three vulnerability indicators are all 0, and the utility value is 0.00; the comprehensive vulnerability parameters of nodes 10 and 9 are relatively small (0.15 and 0.17 respectively), indicating that their vulnerability is relatively low and they are suitable as charging station access nodes; while the utility values of nodes 31 and 18 are 1.00, indicating that their vulnerability is the highest and they are not suitable for accessing high - power loads.

[0073] Node number Power flow distribution entropy Voltage growth entropy Cohesion change rate Comprehensive vulnerability parameter 51 - - - 0.00 52 - - - 0.00 54 - - - 0.00 10 1.69 1.80 1.00 0.15 9 1.69 1.56 0.90 0.17 6 1.45 1.85 0.92 0.18 8 1.40 1.75 0.85 0.18 5 1.19 1.90 0.83 0.19 7 1.17 1.78 0.77 0.19 33 1.42 1.77 0.87 0.20 2 1.18 1.71 0.75 0.21 1 1.09 1.70 0.71 0.22 4 0.99 1.40 0.54 0.33 13 1.10 0.98 0.41 0.38 16 1.10 1.03 0.43 0.39 43 0.97 1.03 0.37 0.42 45 0.97 1.03 0.37 0.42 3 0.62 1.45 0.41 0.45 15 0.69 1.06 0.27 0.57 12 0.69 1.05 0.27 0.58 44 0.69 1.05 0.27 0.58 30 0.70 0.64 0.14 0.60 14 0.00 1.10 0.00 0.64 11 0.00 1.10 0.00 0.65 22 0.43 0.06 0.26 0.76 21 0.32 0.05 0.25 0.78 31 0.00 0.00 0.00 1.00 18 0.00 0.00 0.00 1.00

[0074] Table 1 Results of Vulnerability Indicators of 28 - Node Distribution Network Subsequently, based on the comprehensive vulnerability parameters of each node and the mapping relationship of power supply units, the node with the minimum utility value is selected as the optimal access node within the power supply unit corresponding to each charging station. The selection principle is to preferentially select distribution nodes with relatively small utility values and meeting the engineering construction conditions to minimize the impact of charging station access on the stability of the distribution network. The optimized access results of the charging stations to the distribution nodes are shown in Table 2. Among them, charging station C1 accesses node 33 (comprehensive vulnerability parameter 0.20), charging station C4 accesses node 10 (comprehensive vulnerability parameter 0.15), charging station C6 accesses node 45 (comprehensive vulnerability parameter 0.42), etc. Nodes with relatively low vulnerability within their respective power supply units are selected, effectively ensuring the safe and stable operation of the distribution network.

[0075] Charging station number Node number C1 33 C2 18 C3 16 C4 10 C5 13 C6 45 C7 54 C8 43 C9 51 C10 6

[0076] Table 2 Distribution Nodes Accessed by Charging Stations Through the application of this embodiment, the optimized access of 10 electric vehicle charging stations has been successfully achieved, significantly reducing the adverse impact of charging station access on the stability of the distribution network. At the same time, the charging station layout follows the principles of giving priority to low vulnerability and matching power supply units, providing technical support for the planning of electric vehicle charging infrastructure.

[0077] Embodiment 2, as Figure 3 shown, based on the same inventive concept as the method for selecting charging station access nodes considering the vulnerability of the distribution network provided in Embodiment 1, the present invention embodiment also provides a system for selecting charging station access nodes considering the vulnerability of the distribution network, including: A topology acquisition module 11, configured to acquire the distribution network topology structure of the distribution network, where the distribution network topology structure includes a plurality of distribution nodes; A vulnerability calculation module 12, configured to calculate a plurality of node vulnerability parameter sets of a plurality of distribution network nodes according to the distribution node vulnerability index set; A weight fusion module 13, configured to perform index weight fusion allocation on the distribution node vulnerability index set based on the Jaya-AHP method and the entropy weight method to obtain a vulnerability index weight set; A node optimization module 14, configured to calculate and screen the optimal access node according to the vulnerability index weight set and the plurality of node vulnerability parameter sets for charging station access.

[0078] Further, the topology acquisition module 11 includes the following execution steps: The access node selection method, characterized in that acquiring the distribution network topology structure of the distribution network includes: Acquiring all the plurality of distribution nodes within the distribution network and dividing them into a plurality of power supply units, where each power supply unit includes a plurality of distribution nodes; Construct the mapping relationship between multiple power supply units and multiple charging stations for which access nodes are to be selected, and obtain the distribution network topology structure.

[0079] Further, the vulnerability calculation module 12 includes the following execution steps: Obtain the distribution node vulnerability index set, where the distribution node vulnerability index set includes the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy; Calculate the cohesion change rate of multiple distribution nodes; Calculate the power flow distribution entropy of multiple distribution nodes; Calculate the voltage growth entropy of multiple distribution nodes; Integrate the cohesion change rate, the power flow distribution entropy, and the voltage growth entropy of multiple distribution nodes to obtain a set of vulnerability parameters for multiple nodes.

[0080] Further, the vulnerability calculation module 12 also includes the following execution steps: Define the distribution network as a weighted directed graph G with n nodes and m edges. The adjacency matrix of the weighted directed graph G is as follows: ; HH is the adjacency matrix. If there is a direct connection between distribution node i and distribution node j, then is assigned 1, otherwise 0; Calculate the network cohesion of the weighted directed graph G, as follows: ; where represents the shortest electrical distance between node i and node j, is the set of node points of the weighted directed graph G; According to the network cohesion, calculate the cohesion change rate of multiple distribution nodes, as follows: ; where is the cohesion change rate of the i-th distribution node, represents the subgraph formed after the weighted directed graph G is contracted through distribution node i; is the cohesion of the subgraph.

[0081] Further, the vulnerability calculation module 12 also includes the following execution steps: Calculate the active power change amount of multiple nodes when the unit load changes, as follows: ; where is the active power change amount of the i-th distribution node, is the active power of the i-th distribution node after the unit load change, is the initial active power of the i-th distribution node; Calculate the total amount of power flow impact on the entire network caused by unit load change according to the change in active power, as shown in the following formula: ; where, is the total amount of power flow impact on the entire network caused by unit load change at the i-th distribution node, is the total number of lines; Calculate the power flow transfer impact ratio generated by the unit load change of the distribution node on the line, as shown in the following formula: ; where, is the power flow transfer impact ratio generated by the unit load change at the k-th line and the i-th distribution node; Calculate the power flow distribution entropy according to the power flow transfer impact ratio, as shown in the following formula: ; where, is the power flow distribution entropy of the distribution network generated by the unit load change at the i-th distribution node.

[0082] Furthermore, the vulnerability calculation module 12 further includes the following execution steps: Obtain the voltage amplitude fluctuation amount after the distribution node is disturbed, as shown in the following formula: ; In the formula, is the voltage amplitude of the j-th distribution node in the initial state; is the voltage amplitude of the j-th distribution node when it is disturbed; Calculate the voltage amplitude change rate of the distribution node according to the voltage amplitude fluctuation amount, as shown in the following formula: ; where, is the rated voltage of the j-th distribution node; Calculate the voltage growth entropy according to the voltage amplitude change rate, as shown in the following formula: ; where, is the voltage growth entropy of the j-th distribution node.

[0083] Furthermore, the weight fusion module 13 includes the following execution steps: Based on the Jaya-AHP method, process to obtain an optimized subjective weight column vector; Based on the entropy weight method, process to obtain multiple objective weights of multiple vulnerability indicators of distribution nodes in the vulnerability indicator set of distribution nodes; Based on the optimized subjective weight column vector and multiple objective weights, multiple comprehensive weights are calculated to obtain a vulnerability index weight set, as shown in the following formula: ; where, is the comprehensive weight of the weight of the i-th vulnerability index, is the subjective weight of the weight of the i-th vulnerability index, is the objective weight of the weight of the i-th vulnerability index.

[0084] Furthermore, the weight fusion module 13 further includes the following execution steps: Based on the Jaya-AHP method, a subjective weight objective function is constructed, as shown in the following formula: ; where β is the subjective weight column vector with a dimension of 1×m, and m is the number of indicators of multiple distribution node vulnerability indicators, is the judgment matrix the element in the i-th row and j-th column; Based on the subjective weight objective function, an optimized subjective weight column vector is obtained through optimization processing.

[0085] Furthermore, the node optimization module 14 includes the following execution steps: According to the vulnerability index weight set, multiple node vulnerability parameters in the node vulnerability parameter set of each distribution node are weighted and calculated to obtain multiple comprehensive vulnerability parameters of multiple distribution nodes; According to multiple comprehensive vulnerability parameters, multiple optimal access nodes are screened and accessed within the power supply unit of each charging station.

[0086] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded computers or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices create means for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks for implementing the functions specified in the flow or flows and / or block or blocks.

[0089] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks for implementing the functions specified in the flow or flows and / or block or blocks.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, thereby providing steps for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks for implementing the functions specified in the flow or flows and / or block or blocks.

[0091] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for selecting charging station access nodes considering the vulnerability of the distribution network, characterized in that The method includes: Obtain the distribution network topology of the distribution network, where the distribution network topology includes multiple distribution nodes; Calculate multiple node vulnerability parameter sets of multiple distribution network nodes according to the distribution node vulnerability index set; Based on the Jaya - AHP method and the entropy weight method, perform index weight fusion distribution on the distribution node vulnerability index set to obtain the vulnerability index weight set; According to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculate and screen to obtain the optimal access node for charging station access.

2. The method for selecting a charging station access node considering the vulnerability of the distribution network according to claim 1, wherein Obtain the distribution network topology of the distribution network, including: Obtain all multiple distribution nodes in the distribution network and divide them into multiple power supply units, where each power supply unit includes multiple distribution nodes; Construct the mapping relationship between multiple power supply units and multiple charging stations for which access nodes are to be selected to obtain the distribution network topology.

3. The method for selecting a charging station access node considering the vulnerability of the distribution network according to claim 1, wherein Calculate multiple node vulnerability parameter sets of multiple distribution network nodes according to the distribution node vulnerability index set, including: Obtain the distribution node vulnerability index set, where the distribution node vulnerability index set includes the cohesion change rate, power flow distribution entropy, and voltage growth entropy; Calculate the cohesion change rate of multiple distribution nodes; Calculate the power flow distribution entropy of multiple distribution nodes; Calculate the voltage growth entropy of multiple distribution nodes; Integrate the cohesion change rate, power flow distribution entropy, and voltage growth entropy of multiple distribution nodes to obtain multiple node vulnerability parameter sets.

4. The method for selecting a charging station access node considering the vulnerability of the distribution network according to claim 1, wherein Calculate the cohesion change rate of multiple distribution nodes, including: Define the distribution network as a weighted directed graph G with n nodes and m edges, and the adjacency matrix of the weighted directed graph G is as follows: ； HH is the adjacency matrix. If there is a direct connection between distribution node i and distribution node j, then it is assigned a value of 1; otherwise, it is 0. Calculate the network cohesion of the weighted directed graph G, as follows: ； Among them, represents the shortest electrical distance between node i and node j, is the set of vertices of the weighted directed graph G; According to the network cohesion, calculate the cohesion change rate of multiple distribution nodes, as follows: ； Among them, is the change rate of the cohesion of the i-th distribution node, represents the subgraph formed after the contraction of the weighted directed graph G through the distribution node i; is the cohesion of the subgraph.

5. The method for selecting a charging station access node considering the vulnerability of the distribution network according to claim 1, characterized in that Calculate the power flow distribution entropy of multiple distribution nodes, including: Calculate the change amount of active power when multiple nodes change under unit load, as follows: ； Among them, is the change in active power of the i-th distribution node, is the active power of the i-th distribution node after a unit load change, is the initial active power of the i-th distribution node; According to the change amount of active power, calculate the total power flow impact of the whole network caused by unit load change, as follows: ； Among them, is the total amount of power flow impact on the entire network caused by a unit load change at the i-th distribution node, is the total number of lines; Calculate the power flow transfer impact ratio generated by the distribution node under unit load change for the line, as follows: ； Among them, is the power flow transfer impact ratio generated by a unit load change at the i-th distribution node on the k-th line; According to the power flow transfer impact ratio, calculate the power flow distribution entropy, as follows: ； Among them, is the power flow distribution entropy of the distribution network generated by a unit load change at the i-th distribution node.

6. The method for selecting a charging station access node considering the vulnerability of a distribution network according to claim 1, wherein Calculate the voltage growth entropy of multiple distribution nodes, including: Obtain the voltage amplitude fluctuation amount after the distribution node is disturbed, as follows: ； wherein, is the voltage amplitude of the j-th distribution node in the initial state; is the voltage amplitude of the j-th distribution node when it is disturbed; According to the voltage amplitude fluctuation amount, calculate the voltage amplitude change rate of the distribution node, as follows: ； Among them, is the rated voltage of the jth distribution node; According to the voltage amplitude change rate, calculate the voltage growth entropy, as follows: ； Among them, is the voltage growth entropy of the j-th distribution node.

7. The method for selecting a charging station access node considering the vulnerability of a distribution network according to claim 1, wherein Based on the Jaya - AHP method and the entropy weight method, perform index weight fusion distribution on the distribution node vulnerability index set to obtain the vulnerability index weight set, including: Based on the Jaya - AHP method, process to obtain the optimized subjective weight column vector; Based on the entropy weight method, process to obtain multiple objective weights of multiple distribution node vulnerability indexes in the distribution node vulnerability index set; According to the optimized subjective weight column vector and multiple objective weights, calculate to obtain multiple comprehensive weights to obtain the vulnerability index weight set, as follows: ； Among them, is the comprehensive weight of the weight of the i-th vulnerability index, is the subjective weight of the weight of the i-th vulnerability index, is the objective weight of the weight of the i-th vulnerability index.

8. The method for selecting charging station access nodes considering the vulnerability of the distribution network according to claim 7, characterized in that Based on the Jaya - AHP method, process to obtain the optimized subjective weight column vector, including: Based on the Jaya - AHP method, construct a subjective weight objective function as follows: ； Among them, β is the subjective weight column vector with a dimension of 1×m, where m is the number of indicators of multiple distribution node vulnerability indicators. is the judgment matrix is the element in the i-th row and j-th column of According to the subjective weight objective function, optimize to obtain an optimized subjective weight column vector.

9. The method for selecting a charging station access node considering the vulnerability of the distribution network according to claim 1, characterized in that According to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculate and screen to obtain the optimal access nodes for charging station access, including: According to the vulnerability index weight set, perform weighted calculation on multiple node vulnerability parameters in the node vulnerability parameter set of each distribution node to obtain multiple comprehensive vulnerability parameters of multiple distribution nodes; According to multiple comprehensive vulnerability parameters, screen and obtain multiple optimal access nodes in the power supply unit of each charging station for access.

10. A charging station access node selection system considering the vulnerability of the distribution network, characterized in that, For implementing the method for selecting charging station access nodes considering the vulnerability of the distribution network according to any one of claims 1 to 9, the system includes: A topology acquisition module for acquiring the distribution network topology structure of the distribution network, where the distribution network topology structure includes multiple distribution nodes; A vulnerability calculation module for calculating multiple node vulnerability parameter sets of multiple distribution network nodes according to the distribution node vulnerability index set; A weight fusion module for performing index weight fusion assignment on the distribution node vulnerability index set based on the Jaya - AHP method and the entropy weight method to obtain a vulnerability index weight set; A node optimization module for calculating and screening to obtain the optimal access nodes according to the vulnerability index weight set and the multiple node vulnerability parameter sets for charging station access.

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