Vulnerability assessment method based on hybrid transmission distribution factor

Abstract

The invention discloses a vulnerability assessment method based on a hybrid transmission distribution factor, and belongs to the technical field of interdependent network vulnerability assessment, and the method comprises the steps: constructing a coupling system model comprising a power network layer, a hydrogen energy network layer and a logistics network layer; and establishing an interlayer sensitivity coupling relationship through a coupling weight matrix, and according to the vulnerability evaluation method based on the hybrid transmission distribution factors, a layered sensitivity calculation and cross-layer coupling mapping technology is adopted to form a global hybrid transmission distribution factor (MTDF) matrix, so that the vulnerability of the power network layer, the hydrogen energy network layer and the logistics network layer can be evaluated, and the vulnerability of the power network layer, the hydrogen energy network layer and the logistics network layer can be evaluated. Cross-layer influence quantification of new energy output fluctuation, automatic identification of key fragile nodes and establishment of a cascade fault propagation model are realized, a fragility evaluation index is proposed by calculating L2 norms before and after a fault, and the technical problem of evaluating the coupling fragility of the green port power-hydrogen energy-logistics three-layer network is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vulnerability assessment of interdependent networks, and particularly relates to a vulnerability assessment method based on a hybrid transmission distribution factor. Background Art

[0002] Each energy department should effectively explore its own potential to improve energy efficiency. Common technical means include using alternative clean energy and energy efficiency improvement technologies based on equipment. As an important transportation hub, these energy efficiency improvement means will also become the key technologies for ports to achieve green development. The transportation capacity of the port logistics network depends on weak transportation nodes. By comprehensively using various energy forms such as electricity and hydrogen and realizing the coordinated optimization among multiple energies, the energy utilization efficiency and the capacity of port logistics transportation nodes can be effectively improved. With the deepening of port electrification, the coupling between the logistics system and the energy system is gradually tightened. Energy-logistics coupling is a significant difference between the port integrated energy system and traditional land applications.

[0003] As the pillar of the maritime transportation network connecting value chains and markets around the world, ports are vulnerable to various problems such as extreme weather events and environmental hazards. The transportation capacity of ports after interruption events is particularly important. The vulnerability assessment of single networks such as power grids is already very mature, and the analysis method based on complex network theory has been considered an important means to study complex system problems. For the multi-layer coupled network of ports, after an interruption event occurs, with the coupled cascade relationship between networks, faults spread among multi-layer networks, resulting in a larger area of faults. However, the vulnerability assessment problem of the green port energy flow-logistics coupling network containing hydrogen has a higher dimension and greater difficulty compared with single networks, and there is a lack of accurate assessment indicators. Therefore, it is necessary to develop a new vulnerability assessment method based on a hybrid transmission distribution factor to solve the existing problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a vulnerability assessment method based on a hybrid transmission distribution factor to solve the above problems.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A vulnerability assessment method based on a hybrid transmission distribution factor, for the power network, hydrogen energy network, and logistics network in the port area, perform the following steps S1 - step S6 to complete the vulnerability assessment:

[0006] Step S1: Based on complex network theory, construct a coupled system model including a power network layer, a hydrogen energy network layer, and a logistics network layer;

[0007] Step S2: Calculate the transmission distribution factors of each layer respectively:

[0008] Power network layer: Based on the power transfer distribution factor (PTDF)

[0009] Hydrogen energy network layer: Transmission sensitivity factor (HTDF) based on pipeline flow model

[0010] Logistics network layer: Logistics transmission distribution factor (LTDF) based on transportation flow

[0011] Step S3: Establish an inter-layer sensitivity coupling relationship through a coupling weight matrix, and construct a global mixed transmission distribution factor (MTDF) matrix;

[0012] Step S4: Solve the column vector norm of each node based on the MTDF matrix;

[0013] Step S5: Perform a Monte Carlo simulation including new energy output fluctuations to realize the analysis of the cascading fault propagation model.

[0014] Step S6: Calculate the relative change rate of the spectral norm of the sensitivity matrix based on the column vector norms of the MTDF matrix before and after the fault.

[0015] Preferably, the method for establishing the multi-layer network coupling system model in step S1 is as follows:

[0016] The coupling system model is abstracted into a connected graph G = {D, L, W}. It can be seen that each layer represents a transmission network, the vertices represent the corresponding buses or actual facilities, and the vertices are connected by links, representing the transmission paths of the network at this layer. Different networks are connected by coupling links, representing the energy transmission paths between the networks; specifically, the vertices and links can be described as: power nodes D e , hydrogen nodes D h , logistics equipment nodes D c , power transmission lines L e , gas transmission lines L h , container transportation lines L c , and coupling lines between the networks; the coupling lines are from the starting node to the ending node. The weight W of each link represents the transmission capacity of this path. According to the transmission characteristics of electricity and hydrogen, the power transmission lines and gas transmission lines are two-way transmissions. Due to the particularity of port logistics transportation, within a certain period of time, the direction of the container transportation line is constant. The berth is set as the starting point, and the berth is a type of logistics equipment node. Therefore, the coupling system model is represented by an undirected graph and a directed graph together.

[0017] Preferably, the method for establishing the sensitivity matrix of the hydrogen energy network layer and the logistics network layer in step S2 is as follows:

[0018] Imitating the method for solving the PTDF matrix of the power system, the hydrogen energy network layer is regarded as composed of nodes - pipelines, and solved through the Weymouth equation:

[0019]

[0020] Among them, P i0 is the steady-state pressure, is the steady-state flow rate, and C ij is the pipeline constant, P i and P j represent the pressures at the starting node i and the ending node j;

[0021] Derive the transmission sensitivity factor matrix of the hydrogen energy network layer by analogy with the power transmission distribution factor:

[0022] Construct the admittance matrix: Y ij =-K ij (i≠j);

[0023] Y ij represents the equivalent admittance between node i and node j, and K ij represents the branch admittance coefficient;

[0024] Construct the incidence matrix:

[0025] M ij represents the incidence coefficient matrix of the pipeline nodes.

[0026] Preferably, the construction of the MTDF matrix in step S3 satisfies the following relationship:

[0027]

[0028] Among them, W ph and W hl and W pl represent the electric-hydrogen coupling weight matrix, the hydrogen energy-logistics coupling weight matrix, and the power-logistics coupling weight matrix respectively. The rows of the matrix represent the lines of the coupled system model, the columns represent the nodes of the coupled network, and the diagonal elements of the matrix represent the sensitivity matrices of each layer. Taking the power network layer matrix F p as an example, each element in the matrix represents the sensitivity of the power line to the power node flow rate, and the power network layer matrix F p is the element on the diagonal of the matrix;

[0029] The elements in other positions are the coupling weight matrices between different layers, and their calculation methods include:

[0030] Electric-hydrogen coupling weight matrix:

[0031] W ph (i,j)=P elec,j

[0032] In the formula, i represents the power network layer node, j represents the hydrogen energy network layer node, and P elec,jPower demand of the electrolyzer node;

[0033] Hydrogen energy - logistics coupling weight matrix:

[0034]

[0035] In the formula, k represents the hydrogen energy transfer vehicle node in the logistics network layer, represents the power demand of the hydrogen energy transfer vehicle;

[0036] Electricity - logistics coupling weight matrix:

[0037] W pl (i,n) = P logistic,n

[0038] In the formula, n represents the power - consuming equipment node in the logistics network layer, and P logistic,n is the power demand of the logistics equipment.

[0039] As a preferred technical solution of the present invention, in step S4, calculate the column vector norm of each node:

[0040] When evaluating the network vulnerability, usually calculate the L2 norm for the PTDF column vector of a certain line, such as line l (i.e., the vector composed of the sensitivities of all nodes to this line):

[0041]

[0042] Where: F l represents the PTDF column vector corresponding to the l - th line, k represents the nodes in the network, and N represents the total number of nodes in the network.

[0043] Preferably, the cascading fault propagation model constructed in step S5 is as follows:

[0044] Intra - layer fault triggering

[0045] Power grid: Line power over - limit triggers opening, based on thermal stability constraints

[0046] Disconnection condition:

[0047] Where, P ij represents the actual power of the line, represents the rated power of the line;

[0048] Hydrogen energy network layer: Pipeline flow over - limit triggers isolation,

[0049] Disconnection condition:

[0050] Where, Q pq represents the actual flow of the pipeline, represents the rated flow of the pipeline;

[0051] Logistics network layer: Blocking is triggered when the path load exceeds the limit

[0052] Disconnection condition:

[0053] F k represents the actual logistics volume of path k represents the rated logistics volume of path k;

[0054] Cross-layer fault mapping

[0055] Through the electro-hydrogen coupling weight matrix W ph and the hydrogen energy-logistics coupling weight matrix W pl and the power-logistics coupling weight matrix W hl Transmit the fault impact to the global network:

[0056] Electro-hydrogen cascade effect:

[0057] Q H2,prod = η elec ·∑W ph (i,j)·P bus,i ;

[0058] Among them, Q elec,j represents the power supply of the electrolyzer, and η elec represents the electrolysis efficiency, and P bus,i represents the power of power node i;

[0059] Hydrogen logistics cascade effect::

[0060]

[0061] Among them, represents the working power of the transporter, represents the actual supply power of the transporter;

[0062] Power logistics cascade effect:

[0063]

[0064] Among them, P bus,i represents the power of power node i, and P req represents the rated power of the coupling line.

[0065] The cascade iteration termination conditions include:

[0066] Convergence condition: No new faulty components in two consecutive iterations;

[0067] Forced termination: Reach the preset maximum number of iterations to prevent infinite loop.

[0068] Preferably, the relative change rate of the spectral norm of the sensitivity matrix calculated in step S6 is as follows:

[0069] Calculation formula:

[0070]

[0071] where M pre is the global sensitivity matrix before the fault, i.e., the coupling sensitivity of the power network layer, the hydrogen energy network layer, and the logistics network layer, and M post is the global sensitivity matrix after the fault, and ||·||2 is the spectral norm of the matrix, i.e., the largest singular value, which reflects the most sensitive direction of the system to disturbances.

[0072] Implementation steps:

[0073] Calculation before the fault: Run the electro-hydrogen-logistics coupling system model before the fault, construct the global sensitivity matrix M pre , and calculate its spectral norm;

[0074] Calculation after the fault: After the cascading fault simulation, construct the global sensitivity matrix M post , and calculate its spectral norm;

[0075] Index calculation: Calculate the RSNV value according to the above formula to evaluate the change in network vulnerability.

[0076] Considering the electro-hydrogen-logistics coupling characteristics of the green port electro-hydrogen-logistics coupling network, when evaluating the vulnerability of the system under interruption events, the present invention simultaneously considers the cascading effects of the interruption events in the power network, the hydrogen energy network, and the logistics network.

[0077] Technical effects and advantages of the present invention: The vulnerability assessment method based on the hybrid transmission distribution factor selects the green port power-hydrogen-logistics coupling network as the object of vulnerability assessment. On the basis of the existing research on the vulnerability assessment of interdependent networks, considering the cascading failure propagation impact of the network comprehensively, a comprehensive vulnerability assessment index is proposed. Based on the multi-energy flow coordination characteristics of the green port, an innovative vulnerability assessment model of the power-hydrogen-logistics coupling network is constructed. By quantifying the cascading effect of hydrogen energy transmission fluctuations and logistics transport capacity attenuation caused by power interruption, it overcomes the limitation of ignoring cross-energy coupling vulnerability points in the vulnerability assessment of a single energy network, and realizes the collaborative assessment of the stability of port energy supply and logistics transport capacity under interruption scenarios; based on the cascading effect index RSNV of the L2 norm change rate of the MTDF matrix, it quantifies the cascading failure propagation of interruption events among multi-layer networks, and conducts vulnerability assessment based on the sensitivity of the line to the node flow, which can accurately reflect the vulnerability of the port coupling network and lay a solid foundation for guiding the construction of the network topology structure of the hydrogen-containing green port; by constructing a three-layer network coupling model of power-hydrogen-energy-logistics, using hierarchical sensitivity calculation and cross-layer coupling mapping technology, a global hybrid transmission distribution factor (MTDF) matrix is formed to realize the quantification of the cross-layer impact of new energy output fluctuations, the automatic identification of key vulnerable nodes and the establishment of a cascading failure propagation model, and a vulnerability assessment index is proposed by calculating the L2 norm before and after the fault, which solves the technical problem of assessing the coupling vulnerability of the three-layer network of power-hydrogen-energy-logistics in the green port. Description of the Drawings

[0078] Figure 1 It is a schematic flow chart of the method of the present invention;

[0079] Figure 2 It is a schematic diagram of the power-hydrogen-logistics coupling in the green port of the present invention;

[0080] Figure 3 It is a graph of 500 wind power scenarios generated by Latin hypercube provided by the embodiment of the present invention;

[0081] Figure 4 It is a graph of 500 photovoltaic scenarios generated by Latin hypercube provided by the embodiment of the present invention;

[0082] Figure 5 It is a graph of 5 wind-solar scenarios after K-means clustering reduction provided by the embodiment of the present invention;

[0083] Figure 6 It is a graph of 5 photovoltaic scenarios after K-means clustering reduction provided by the embodiment of the present invention;

[0084] Figure 7 It is a graph of the node column vector norm before the fault provided by the embodiment of the present invention;

[0085] Figure 8 This is the post-fault node column vector norm graph provided by the embodiments of the present invention. Detailed implementation manners

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0087] The present invention provides a vulnerability assessment method based on a hybrid transmission allocation factor as shown in Figure 1 . For the green port power-hydrogen-logistics coupling network affected by interruption events, the green port power-hydrogen-logistics coupling network is synthesized by a power network, a hydrogen energy network, and a logistics network, and the following steps S1-S6 are executed to complete the comprehensive assessment of the vulnerability of the coupled cascaded interdependent network system:

[0088] Step S1: Construct a coupling network model, and the specific steps are as follows:

[0089] The green port power-hydrogen-logistics coupling network includes a power network topology structure diagram, a hydrogen energy transmission network structure diagram, and a container logistics transmission network structure diagram. Specifically, vertices and links can be described as: power node D e , hydrogen node D h , logistics equipment node D c , transmission line L e , gas transmission line L h , container transportation line L c and the coupling lines between networks, and the coupling lines are from the starting node to the ending node.

[0090] Step S2: In the power network, a PTDF matrix reflecting the sensitivity of line flow to node flow can be constructed by the admittance matrix and the incidence matrix. By analogy with this method, the HTDF matrix of the hydrogen energy network and the LTDF matrix of the logistics network are solved respectively.

[0091] Step S2.1: Adopt a 30-node power network, and simulate the real situation of the port by modifying the generator data. Adopt wind turbines and photovoltaic power generation to meet the daily operation needs of the port and achieve off-grid operation. Solve the PTDF matrix of the power network through the admittance matrix and the incidence matrix of the power network.

[0092] Step S2.2: Calculate and solve through the Weymouth equation:

[0093]

[0094] Among them, P i0 is the steady-state pressure, is the steady-state flow rate, and C ij is the pipeline constant P i 、P j represent the pressures at the starting node i and the ending node j;

[0095] Derive the transmission sensitivity factor matrix of the hydrogen energy network layer by analogy with the power transmission distribution factor:

[0096] 1. Construct the admittance matrix: Y ij =-K ij (i≠j)

[0097] Y ij represents the equivalent admittance between node i and node j, and K ij represents the branch admittance coefficient;

[0098] 2. Construct the incidence matrix: M ij represents the incidence coefficient matrix of the pipeline nodes;

[0099] Finally, substitute into the formula to solve: HTDF = M · (Y')-1

[0100] Step S2.3: The port logistics network uses berths, quay cranes, transfer vehicles, and yard cranes as container transmission nodes. The container transmission direction is berth → quay crane → transfer vehicle → yard crane. A full connection method is adopted between different types of equipment. Subsequently, construct the admittance matrix of the logistics network and calculate the LTDF matrix;

[0101] Improved BPR function:

[0102]

[0103] In the formula, R represents the solution path impedance, Q is the current flow rate, and Q cap is the path capacity, and α, β are BPR parameters, with the default values of α = 0.15 and β = 4.

[0104] Step S3: The port logistics system is different from the traditional transportation system. The logistics network transports containers and does not have the characteristics of an energy carrier. Therefore, the energy demand in the logistics network is fixed at the logistics network node facilities. The logistics equipment, quay cranes and yard cranes are powered by the power grid. The berth is also powered by the power grid due to the shore power facility. The transfer vehicle is hydrogenated by the hydrogen network. From this, the coupling relationship of the three-layer network can be obtained. Its calculation method includes: the electro-hydrogen coupling weight:

[0105] W ph (i,j)=P elec,j

[0106] Let \(i\) represent the nodes in the power network layer and \(j\) represent the nodes in the hydrogen energy network layer, \(P\) elec,j Power demand of the electrolyzer node;

[0107] Hydrogen energy logistics coupling weight:

[0108]

[0109] In the formula, \(k\) represents the hydrogen energy transfer vehicle node in the logistics network layer, represents the power demand of the hydrogen energy transfer vehicle;

[0110] Power logistics coupling weight:

[0111] W pl (i,n) = P logistic,n

[0112] In the formula, \(n\) represents the electrical equipment node in the logistics network layer, \(P\) logistic,n is the power demand of the logistics equipment.

[0113] Step S3.1: According to the sensitivity matrix and network coupling relationship of each layer of the network, the global hybrid transmission distribution factor MTDF matrix can be constructed as follows:

[0114]

[0115] Among them, \(W\) ph , \(W\) hl , \(W\) pl represent the electricity-hydrogen coupling weight matrix, hydrogen energy-logistics coupling weight matrix, and power-logistics coupling weight matrix respectively. The rows of the matrix represent the lines of the coupling network, and the columns represent the nodes of the coupling network. \(F\) p , \(F\) h , \(F\) l represent the diagonal elements in the power network layer sensitivity matrix, hydrogen energy network layer sensitivity matrix, and logistics network layer sensitivity matrix respectively, indicating the sensitivity matrices of each layer, and the remaining elements are obtained from the sensitivity matrices of each layer and the coupling relationship.

[0116] Step S4: For each node, calculate the norm of the corresponding column in the MTDF as the vulnerability index of the node. Sort according to the vulnerability index of the node to identify the key nodes that have the greatest impact on the entire coupling network.

[0117] The calculation method of the L2 norm of the column node is as follows:

[0118]

[0119] In the formula, \(F\) l represents the PTDF column vector corresponding to the \(l\)-th line, \(k\) represents the nodes in the network, and \(N\) represents the total number of nodes in the network.

[0120] Step S5: After the green port power-hydrogen-logistics coupling network encounters an interruption event, some nodes fail first. The Monte Carlo simulation is used to generate the initial faults, and the cascade fault propagation model is used to simulate the propagation process of faults among the power network layer, hydrogen energy network layer, and logistics network layer. The specific process is shown in the figure.

[0121] Step S5.1: The process of Monte Carlo simulation of initial faults is as follows:

[0122] 1. Generate scenarios by Latin hypercube sampling

[0123] (1) Determine the number of samples m, which is set to generate 500 scenarios in this paper.

[0124] (2) Determine the distribution function of power generation.

[0125] (3) Divide the equivalent region of the power generation distribution function into m intervals, and the probability of each region is equal.

[0126] (4) Independently and randomly draw samples from different intervals and record them. Ensure that only 1 sample appears in each interval.

[0127] Shuffle the samples and output the generated results as shown in the figure

[0128] Among them, the Weibull distribution is used to generate the fan scenarios, and the Beta distribution is used to generate the photovoltaic scenarios

[0129] 2. Reduce scenarios by K-means clustering

[0130] (1) Select the number of clusters n to be divided into, which is set to cluster and reduce to 5 representative scenarios in this paper.

[0131] (2) Randomly select 5 data points from the scenario dataset as the initial centroids.

[0132] (3) For each scenario point, calculate the distance between it and each centroid, and then assign it to the cluster with the closest distance. The distance is calculated by the Euclidean distance method.

[0133] (4) For each divided cluster, calculate the average value of all data points in the cluster and use this average value as the new centroid.

[0134] (5) Repeat steps (3) and (4) until the cluster assignment no longer changes or reaches the maximum number of iterations

[0135] Output the 5 cluster center points, which are 5 representative scenarios. The five fan and photovoltaic scenarios after clustering are shown in the figure

[0136] Step S5.2: As a preferred technical solution of the present invention, the constructed cascading fault propagation model is as follows:

[0137] 1. Intra-layer fault triggering

[0138] Power grid layer: Line power overlimit triggers opening (based on thermal stability constraint)

[0139] Disconnection condition:

[0140] where P ij represents the actual power of the line, represents the rated power of the line;

[0141] Hydrogen energy network layer: Pipeline flow overlimit triggers isolation

[0142] Disconnection condition:

[0143] where Q pq represents the actual flow of the pipeline, represents the rated flow of the pipeline;

[0144] Logistics network layer: Path load overlimit triggers blockage

[0145] Disconnection condition:

[0146] where F k represents the actual logistics volume of path k, represents the rated logistics volume of path k

[0147] 2. Cross-layer fault mapping

[0148] Through the electro-hydrogen coupling weight matrix W ph 、hydrogen energy-logistics coupling weight matrix W pl 、power-logistics coupling weight matrix W hl the fault impact is transmitted to other layers:

[0149] Electro-hydrogen cascading effect:

[0150] Q elec,k = η elec ·∑W ph (i,j)·P bus,i

[0151] where Q elec,j represents the power supply of the electrolyzer, η elec represents the electrolysis efficiency, P bus,i represents the power of power node i;

[0152] Hydrogen logistics cascading effect:

[0153]

[0154] Among them, represents the working power of the transfer vehicle, represents the actual supply power of the transfer vehicle;

[0155] Power logistics cascade effect:

[0156]

[0157] Among them, P bus,i represents the power of power node i, and P req represents the rated power of the coupling line;

[0158] 3. The cascade iteration termination conditions include:

[0159] Convergence condition: No new faulty components in two consecutive iterations;

[0160] Forced termination: Reach the preset maximum number of iterations to prevent infinite loop;

[0161] Step S6: Calculate the relative change rate of the spectral norm of the sensitivity matrix based on the column vector norms before and after the cascade fault. The calculation formula is as follows:

[0162]

[0163] Among them, M pre is the global sensitivity matrix before the fault, that is, the coupling sensitivity of the power network layer, hydrogen energy network layer, and logistics network layer. M post is the global sensitivity matrix after the fault, and ||·||2 is the spectral norm of the matrix, that is, the largest singular value, reflecting the most sensitive direction of the system to perturbations.

[0164] Implementation steps:

[0165] 1. Pre-fault calculation: Run the electro-hydrogen-logistics coupling system model before the fault, construct the global sensitivity matrix M pre , and calculate its spectral norm;

[0166] 2. Post-fault calculation: After simulating the cascade fault, construct the global sensitivity matrix M post , and calculate its spectral norm;

[0167] 3. Index calculation: Calculate the RSNV value according to the formula to evaluate the change in network vulnerability.

[0168] The following is an embodiment of the present invention. Based on the vulnerability assessment method of the green port electricity-hydrogen-logistics coupling system based on the hybrid transmission allocation factor designed by the present invention, this method comprehensively evaluates the vulnerability of the green port electricity-hydrogen-logistics coupling network under interruption events from three levels: electricity, hydrogen energy, and logistics. This embodiment conducts analysis and demonstration in combination with specific data:

[0169] Figure 1 is the flowchart of the vulnerability assessment method, Figure 2 is the schematic diagram of the green port electricity-hydrogen-logistics coupling. In the power network, wind turbines and photovoltaic power serve as power sources to provide power support for the system. In the hydrogen energy network, electrolyzers use grid power to produce hydrogen to support the hydrogen load in the system. Among them, grid nodes 6 and 22 supply electrical energy to hydrogen network nodes 1 and 2. Hydrogen network nodes 4-7 transport hydrogen to logistics network nodes 6-11. Grid nodes 1 and 2 supply shore power to logistics network nodes 1 and 2. Grid nodes 3-5 supply electrical energy to logistics network nodes 3-5. Grid nodes 12-15 supply electrical energy to logistics network nodes 12-15.

[0170] Through step S1, the IEEE 30-node power network, 8-node hydrogen energy network, and 15-node port logistics network are abstracted into a connected graph G = {D, L, W}. Specifically, vertices and links can be described as: power nodes D e , hydrogen nodes D h , logistics equipment nodes D c , transmission lines L e , gas transmission lines L h , container transportation lines L c .

[0171] For the power network, the method in step S2.1 is used to solve its PTDF matrix; for the hydrogen energy network, the improved Weymouth equation flow resistance in step S2.2 is used to calculate the hydrogen pipeline flow, and the node-pipeline incidence matrix and the admittance matrix of the hydrogen energy network are constructed. Finally, the HTDF matrix is constructed; for the logistics network, the BPR function in step S2.3 is used to solve the modified impedance. The container transmission direction is berth → quay crane → transfer vehicle → yard crane. A fully connected method is used between different types of equipment. Subsequently, the admittance matrix of the logistics network is constructed and the LTDF matrix is calculated.

[0172] Through step S3, the sensitivity matrices of the electricity-hydrogen coupling, electricity-logistics coupling, and hydrogen energy-logistics coupling relationships are constructed respectively. Subsequently, the global hybrid transmission allocation factor matrix MTDF is constructed. The rows of the matrix represent the lines of the coupling network, and the columns represent the nodes of the coupling network. The diagonal elements in the matrix represent the sensitivity matrices of each layer.

[0173] Next, the elements in the MTDF matrix are processed using step S4. By calculating the column vector norms in the MTDF matrix as the vulnerability indicators for each point, and sorting according to the vulnerability indicators of the nodes, the key nodes with greater influence are identified as shown in Table 1.

[0174] Table 1 Key nodes with the largest vulnerability indicators in each layer of the network

[0175] Network layer Node number Vulnerability index Power network layer 22 9.3838 Hydrogen energy network layer 2 14.5867 Logistics network layer 2 2.2700

[0176] After calculating the node norms of the initial network, the initial faults are simulated using Monte Carlo in step S5, and the propagation process of faults between the power network layer, hydrogen energy network layer, and logistics network layer is simulated through the cascading fault propagation model. Using historical data, 500 wind power outputs following the Weibull distribution and photovoltaic outputs following the Beta distribution are generated as Figure 3 、 Figure 4 shown, and then reduced to five scenarios using K-means clustering as Figure 5 、 Figure 6 shown. Subsequently, the wind turbine and photovoltaic outputs are added to the cascading fault model for simulating the fault propagation after the interruption event, and the node column vector norms after the fault are obtained through steps S3 and S4 as Figure 7 、 Figure 8 shown.

[0177] Finally, based on the node column vector norms before and after the fault, the relative change rate of the spectral norm of the sensitivity matrix in step S6 is calculated to be 15.69, and the change rate is shown in Table 2. The vulnerability of different green port power-hydrogen-logistics coupling networks can be judged by the relative change rate of the spectral norm of the sensitivity matrix RSNV.

[0178] Table 2 Change rate of spectral norm before and after the fault

[0179]

[0180]

[0181]

[0182]

[0183] The vulnerability assessment method based on the mixed transfer distribution factor selects the green port electricity-hydrogen-logistics coupling network as the object of vulnerability assessment. On the basis of existing research on the vulnerability assessment of interdependent networks, considering the cascading failure propagation impact of the network comprehensively, a comprehensive vulnerability assessment index is proposed. Based on the multi-energy flow coordination characteristics of the green port, an innovative vulnerability assessment model of the electricity-hydrogen-logistics coupling network is constructed. By quantifying the cascading effect of hydrogen energy transmission fluctuations and logistics capacity attenuation caused by power outages, it overcomes the limitation of ignoring cross-energy coupling vulnerability points in the vulnerability assessment of a single energy network, and realizes the collaborative assessment of the stability of port energy supply and logistics transportation capacity under interruption scenarios; based on the cascading effect index RSNV of the L2 norm change rate of the MTDF matrix, it quantifies the cascading failure propagation of interruption events between multi-layer networks, and conducts vulnerability assessment based on the sensitivity of the line to the node flow, which can accurately reflect the vulnerability of the port coupling network and lay a solid foundation for guiding the construction of the network topology of the hydrogen-containing green port; by constructing a three-layer network coupling model of electricity-hydrogen-logistics, adopting hierarchical sensitivity calculation and cross-layer coupling mapping technology, a global mixed transfer distribution factor (MTDF) matrix is formed to realize the quantification of the cross-layer impact of new energy output fluctuations, the automatic identification of key vulnerable nodes and the establishment of a cascading failure propagation model. By calculating the L2 norm before and after the fault, a vulnerability assessment index is proposed, which solves the technical problem of assessing the coupling vulnerability of the three-layer network of electricity-hydrogen-logistics in the green port.

[0184] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A vulnerability assessment method based on a hybrid transmission allocation factor, characterized in that: Including: Constructing a coupled system model including a power network layer, a hydrogen energy network layer, and a logistics network layer; Respectively calculating the transmission distribution factors of the power network layer, the hydrogen energy network layer, and the logistics network layer: Establishing an inter-layer sensitivity coupling relationship through a coupling weight matrix, and constructing a global mixed transmission distribution factor matrix; Solving the column vector norm of each node based on the global mixed transmission distribution factor matrix; Simulating the failure probability of new energy output; Calculating the relative change rate of the sensitivity matrix spectral norm based on the column vector norms of the global mixed transmission distribution factor matrix before and after the failure.

2. The vulnerability assessment method based on the hybrid transmission allocation factor according to claim 1, wherein: The step of respectively calculating the transmission distribution factors of the power network layer, the hydrogen energy network layer, and the logistics network layer includes: the power network layer is calculated based on the power transfer distribution factor; the hydrogen energy network layer is calculated based on the transmission sensitivity factor of the pipeline flow; the logistics network layer is calculated based on the logistics transmission distribution factor of the transportation flow.

3. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 1, characterized in that: The construction method of the described coupling system model includes: constructing a connected graph G = {D, L, W}, where the vertices include: power nodes D e , hydrogen nodes D h and logistics equipment nodes D c ; the links include: power transmission lines L e , gas transmission lines L h , container transportation lines L c and the coupling lines between the networks; The coupling line is from the starting node to the ending node; the weight W of each link represents the transmission capacity of the path, and the power transmission line L e and the gas transmission line L h are for two-way transmission, and the container transmission line L c has a constant direction, and the berth is set as the starting point.

4. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 1, characterized in that: The method for establishing the sensitivity matrix of the hydrogen energy network layer and the logistics network layer includes: Setting the hydrogen energy network layer as node-pipeline, and calculating and solving through the Weymouth equation: Among them, P i0 represents the steady-state pressure, represents the steady-state flow rate, C ij represents the pipeline constant, P i 、P j represent the pressures at the starting node i and the ending node j; Analogously deriving the transmission sensitivity factor matrix of the hydrogen energy network layer through the power transmission distribution factor includes: Construct the admittance matrix: Y ij = -K ij (i ≠ j); Y ij represents the equivalent admittance between node i and node j, and K ij represents the branch admittance coefficient; Construct an association matrix: M ij Represents the correlation coefficient matrix of pipeline nodes.

5. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 1, characterized in that: The construction of the global mixed transmission distribution factor matrix satisfies the following relationship: Among them, W ph , W hl , W pl respectively represent the electricity-hydrogen coupling weight matrix, the hydrogen energy-logistics coupling weight matrix, and the electricity-logistics coupling weight matrix; the rows of the matrix represent the lines of the coupling system model, and the columns represent the nodes of the coupling network, F p , F h , F l respectively represent the sensitivity matrix of the power network layer, the sensitivity matrix of the hydrogen energy network layer, and the sensitivity matrix of the logistics network layer; Power-hydrogen coupling weight matrix: W ph (i,j) = P elec,j i represents the node in the power grid layer, and j represents the electrolyzer node in the hydrogen energy network layer. P elec,j represents the power demand of the electrolyzer node; Hydrogen energy-logistics coupling weight matrix: W hl (j, k) = Q H2,k Wherein, k represents the hydrogen energy transfer vehicle node in the logistics network layer, and Q H2,k represents the power demand of the hydrogen energy transfer vehicle; Power-logistics coupling weight matrix: W pl (i,n) = P logistic,n In the formula, n represents the power consumption equipment nodes of the logistics network layer, and P logistic,n is the power demand of the logistics equipment.

6. The vulnerability assessment method based on the hybrid transmission allocation factor according to claim 1, wherein: The step of solving the column vector norm of each node includes: When evaluating the network vulnerability, calculating the L2 norm for the PTDF column vector of the l-th line: Among them, F l represents the PTDF column vector corresponding to the l-th line, k represents the nodes in the network, and N represents the total number of nodes in the network.

7. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 1, characterized in that: The cascading fault propagation model includes: Intra-layer fault triggering: Power grid layer: Line power overlimit triggers opening, opening condition: Among them, P ij represents the actual power of the line, and represents the rated power of the line; Hydrogen energy network layer: Isolation is triggered when the pipeline flow exceeds the limit. Disconnection conditions: Among them, Q pq represents the actual flow rate of the pipeline, and represents the rated flow rate of the pipeline; Logistics network layer: Blocking is triggered when the path load exceeds the limit. Disconnection condition: Among them, F k represents the actual logistics volume of path k, and F k max represents the rated logistics volume of path k.

8. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 7, characterized in that: The cascading fault propagation model also includes: cross-layer fault mapping: Through the electro-hydrogen coupling weight matrix W ph and the hydrogen energy-logistics coupling weight matrix W pl and the electricity-logistics coupling weight matrix W hl transmit the fault impact to the global network: Power-hydrogen cascading effect: Q elec,k = η elec ·∑W ph (i,j)·P bus,i Among them, Q elec,j represents the power supply of the electrolyzer, η elec represents the electrolysis efficiency, P bus,i represents the power of power node i; Hydrogen energy-logistics cascading effect: Among them, represents the working power of the transfer vehicle, and Q H2,k represents the actual supply power of the transfer vehicle; Power-logistics cascading effect: Among them, \(P_i\) bus,i represents the power of power node \(i\), and \(P_{ij}\) req represents the rated power of the coupling line.

9. The vulnerability assessment method based on a hybrid transmission allocation factor according to claim 8, wherein: The cascading fault propagation model also includes: cascading iteration termination conditions: Convergence condition: no new fault nodes are added in two consecutive iterations; Forced termination: reaching the preset maximum number of iterations.

10. A vulnerability assessment method based on a hybrid transmission allocation factor according to claim 1, characterized in that: The step of calculating the relative change rate of the sensitivity matrix spectral norm includes: Pre-fault calculation: Run the electro-hydrogen logistics coupling system model before the fault, and construct the pre-fault global sensitivity matrix M pre , and calculate its spectral norm; Post-fault calculation: After cascading fault simulation, construct the post-fault global sensitivity matrix M post , and calculate its spectral norm; Index calculation: calculating the RSNV value through the following formula to evaluate the change of network vulnerability; Among them, M pre is the pre-fault global sensitivity matrix, and M post is the post-fault global sensitivity matrix, and ||·||2 is the spectral norm of the matrix.

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