Port toughness optimization method and system based on traffic network and power grid coupling model

Abstract

The invention relates to a port toughness optimization method and system based on a traffic network and power grid coupling model. The method comprises the steps of obtaining system configuration and structure parameters of a traffic network and a power grid of a port; calculating the container throughput of the port in the initial state before an extreme event occurs in the port; after an extreme event occurs in the port, according to the traffic network and power grid coupling model, gradually calculating the operation state of a coupling network formed by the traffic network and the power grid at each moment, and calculating the container throughput of the port in the lowest state; after the port is repaired, a recovery strategy is obtained step by step according to the traffic network and power grid coupling model so as to repair the port, and the container throughput reaching a stable state after the port is recovered is obtained; and according to the container throughput of the port in the initial state, the lowest state and the stable state, calculating a port toughness index so as to carry out port configuration optimization. Compared with the prior art, the method realizes accurate and complete quantitative evaluation and optimization of port toughness.

Description

Technical Field

[0001] This invention relates to the field of port scheduling optimization technology, and in particular to a port resilience optimization method and system based on a transportation network and power grid coupling model. Background Technology

[0002] As a key hub in the global supply chain, the stable operation of ports is crucial to national economy and security. In recent years, the frequent occurrence of low-probability, high-impact extreme events such as earthquakes and typhoons has posed a severe challenge to ports' resilience and ability to recover quickly. Port operations inherently rely on multiple interconnected functional networks, among which transportation networks (such as roads, railways, and waterways) and power distribution networks are two core physical infrastructures. These two networks are interdependent and closely coupled: the power network provides energy for transportation equipment (such as cranes, lighting, and control systems), while the smooth operation of the transportation network is related to the maintenance and repair of the power equipment. However, existing technologies lack a modeling framework capable of systematically and comprehensively assessing the overall resilience of ports within this coupled network.

[0003] Current research primarily focuses on performance evaluation or resilience analysis of individual port networks (such as independent transportation or power grids). For example, some studies simulate traffic flow changes in transportation networks under disaster conditions, while others use power system analysis software to assess grid vulnerability. These methods typically treat each network as an isolated system, modeling and optimizing only its internal structure and function.

[0004] The above solution has the following drawbacks: 1. Isolated analytical perspective: Existing methods fail to adequately consider the coupling and interdependence between the port's internal transportation network and power network. For example, they cannot simulate how the paralysis of terminal cranes due to power outages can further trigger congestion in the transportation network; conversely, they cannot assess the cascading effects of road closures preventing maintenance personnel from reaching the site, thus delaying power restoration.

[0005] 2. Incomplete assessment framework: Due to the lack of overall modeling of the coupled network, existing technologies are unable to provide a comprehensive quantitative framework to fully and accurately characterize the overall performance degradation and recovery process of ports under extreme events, i.e., the comprehensive resilience of ports.

[0006] 3. Insufficient quantification methods: Existing evaluation methods for single networks are difficult to integrate multi-dimensional data such as "node failure curves" and "extreme event graphs" to accurately quantify the dynamic performance degradation of coupled networks under disasters, resulting in inaccurate and impractical evaluation results. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art, which leads to inaccurate and incomplete port assessments due to isolated analytical perspectives, and to provide a port resilience optimization method and system based on a transportation network and power grid coupling model.

[0008] The objective of this invention can be achieved through the following technical solutions: A port resilience optimization method based on a transportation network and power grid coupling model includes: Obtain the system configuration and structural parameters of the transportation and power networks of the port to be evaluated; Calculate the container throughput of the port in its initial state before extreme events occur; After an extreme event occurs at the port, based on a pre-established coupled model of the transportation network and the power grid, the operating status of the coupled network consisting of the transportation network and the power grid is calculated step by step at each time point, and the minimum container throughput of the port is calculated. After the port is repaired, a recovery strategy is gradually obtained based on the coupling model of the transportation network and the power grid to repair the port and obtain the container throughput when the port reaches a stable state after recovery. The port resilience index is calculated based on the container throughput of the port in its initial, minimum, and steady states to optimize port configuration.

[0009] Furthermore, the formula for calculating the port resilience index is as follows: In the formula, Initial state Container throughput at any given time Interruption event After it happened The container throughput that always reaches its minimum level. Interruption event Recovery after occurrence Container throughput at any given time Let be the port resilience index of the port network at time t.

[0010] Furthermore, the transportation network and power grid coupling model includes a transportation network optimization model and a power network optimization model, and sets up the interaction between the transportation network optimization model and the power network optimization model to achieve coupling; The interactions include: the accessibility of damaged nodes in the power grid depends on the road conditions of the transportation network, and the functional operation of the transportation equipment contained in the transportation network requires energy support from the power grid.

[0011] Furthermore, the transportation network optimization model takes the maximum number of containers that can be transported on the path from the starting point S to the ending point T as the optimization objective. The corresponding constraints include the constraint that the cargo output of each node must not exceed its processing capacity, the constraint that the number of port equipment can only have a single configuration scheme, and the constraint that the input and output flow are kept in balance. The power network optimization model takes maximizing power transmission as its constraint objective. The corresponding constraints include the power flow relationship constraints between power sources, switching nodes, and load nodes; the constraint to ensure that the power flow after an interruption does not exceed the line transmission limit; and the constraint to ensure that the output of the power generation unit does not exceed its capacity limit.

[0012] Furthermore, during the degradation phase following an extreme event at the port, the method calculates the substation output of the power network at each moment. If the substation output meets the corresponding load demand, the power network's energy storage system continues to charge; otherwise, the energy storage system discharges. If the energy storage system's discharge still cannot meet the corresponding load demand, then the distributed power generation of the power grid is activated. If the distributed power generation also cannot meet the corresponding load demand, then the power grid begins to degrade. When the load of the power grid reaches its lowest point, the corresponding degradation index is calculated, and the remaining available capacity of all types of components is calculated based on the failure rate of the transportation components. Based on this, the scheduling optimization is carried out again through the transportation network and power grid coupling model to determine the corresponding maximum container throughput.

[0013] Furthermore, the expression for calculating the degradation index is as follows: In the formula, The degradation index, The moment when degradation begins. For the stable moment after degradation, This represents the load requirements in the initial state. for t Current load requirements; The formula for calculating the remaining available capacity is: In the formula, This represents the percentage of remaining component capacity after being affected by a combined interruption event and power failure. The damage rate of transportation components due to extreme events; the subscript 'o' represents the set of transportation network components. This is a proportionality coefficient used to indicate the degree of recovery of transportation network components relative to their original capacity. This is the original capacity of the component. To improve efficiency, The duration of the disruption caused by extreme events. For components in The ability to recover in a short time.

[0014] Furthermore, at each moment after the port repair work is carried out, the method calculates the corresponding standardized recovery efficiency index based on the power grid load recovery status, thereby calculating the joint recovery efficiency to determine the remaining available capacity of all types of components. Based on this, the scheduling is re-optimized through the transportation network and power grid coupling model to determine the corresponding maximum container throughput.

[0015] Furthermore, the formula for calculating the standardized recovery efficiency index is as follows: In the formula, To standardize the recovery efficiency index, This is a point in time when the power grid has been restored. This marks the moment when the power grid officially enters the recovery phase. This represents the load requirements in the initial state. For the load demand in the steady state after power grid degradation, for t Current load requirements; The formula for calculating the combined recovery efficiency is: In the formula, To improve overall recovery efficiency, the subscript 'o' represents a set of transportation network components. For preference parameters, For the recovery rate of the transport components themselves; The formula for calculating the remaining available capacity is: In the formula, This represents the percentage of remaining component capacity after being affected by a combined interruption event and power failure. The damage rate of the transport components themselves due to extreme events. To indicate the expected recovery efficiency of a component, This is the original capacity of the component. The duration of the disruption caused by extreme events. For components in The ability to recover in a short time.

[0016] Furthermore, the method involves setting the desired recovery efficiency for all components. and recovery time The optimal scheduling scheme and the corresponding maximum container throughput of the coupled transportation network and power grid model are obtained, thereby determining the corresponding port resilience index.

[0017] This invention also provides a port resilience optimization system for implementing the above-mentioned port resilience optimization method based on a transportation network and power grid coupling model, comprising: The data acquisition module is used to acquire the system configuration and structural parameters of the transportation network and power network of the port to be evaluated; The port initial state calculation module is used to calculate the container throughput of the port in its initial state before extreme events occur. The port minimum state calculation module is used to calculate the operating state of the coupled network consisting of the transportation network and the power grid at each time step by step, based on a pre-established transportation network and power grid coupling model, after an extreme event occurs at the port, and to calculate the container throughput of the port in the minimum state. The port recovery status calculation module is used to gradually obtain recovery strategies based on the transportation network and power grid coupling model after the port has been repaired, so as to repair the port and obtain the container throughput when the port reaches a stable state after recovery. The port resilience optimization module is used to calculate the port resilience index based on the container throughput of the port in the initial, minimum, and steady states, in order to optimize the port configuration.

[0018] Compared with the prior art, the present invention has the following advantages: (1) This invention establishes for the first time a port resilience assessment framework that couples transportation networks and power networks. Through optimized calculations and network flow analysis under normal, fault, and recovery states, it can simulate the mutual influence between the two networks, thereby reflecting the overall resilience of the port more realistically and comprehensively. The port resilience index is presented in the form of the ratio of the degree of loss of the port network during the recovery state to that during the fault state, thus realizing the accurate quantitative assessment and optimization of port resilience.

[0019] (2) This invention is based on two types of interactions between the transportation network and the power network: the accessibility of damaged nodes in the power network depends on the traffic conditions of the roads in the transportation network, and the operation of the transportation equipment contained in the transportation network requires the power network to provide energy support, thus realizing the precise coupling of the two networks.

[0020] (3) In the degradation phase after an extreme event occurs at a port, the present invention provides a degradation index and the calculation of the remaining available capacity of all types of components based on the failure rate of transportation components; after the port is repaired, it provides a standardized recovery efficiency index, joint recovery efficiency and the calculation of the remaining available capacity of all types of components. It can accurately quantify the performance degradation of each network and its coupled system in a disaster, making resilience assessment move from qualitative to quantitative, and the results are more scientific and reliable.

[0021] (4) The framework proposed in this invention can not only be used for the “post-event” assessment of port resilience, but also provide “pre-event” decision support for the reinforcement of port infrastructure, the pre-positioning of emergency resources and the formulation of recovery strategies by simulating different extreme scenarios (such as two different extreme weather events), effectively improving the scientific level of port planning and operation management. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the process from damage to recovery of toughness provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a port transportation network provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the change of power grid demand load over time, provided in an embodiment of the present invention. Figure 4 This is an example of constructing a Bayesian network provided in an embodiment of the present invention; Figure 5 This is a network diagram based on field investigation in one example provided in this embodiment of the invention; Figure 6 This is a schematic diagram of the combined wind and solar power output curve used in one example provided in this embodiment of the invention; Figure 7 This is a schematic diagram illustrating the failure probability of a busbar and tower under different extreme event levels, provided in an embodiment of the present invention. Figure 8 This is a schematic diagram illustrating the change of extreme event levels over time, provided in an embodiment of the present invention. Figure 9 This is a schematic diagram of power grid load demand under extreme event impact provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the resilience assessment result curve of a use case provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of a port resilience optimization method based on a transportation network and power grid coupling model provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0026] Example 1 like Figure 11 As shown, this embodiment provides a port resilience optimization method based on a transportation network and power grid coupling model, including: S1: Obtain the system configuration and structural parameters of the transportation and power networks of the port to be evaluated; S2: Calculate the container throughput of the port in its initial state before an extreme event occurs. S3: After an extreme event occurs at the port, based on the pre-established transportation network and power grid coupling model, the operating state of the coupled network consisting of the transportation network and power grid is calculated step by step at each time point, and the minimum container throughput of the port is calculated. The transportation network and power grid coupling model includes a transportation network optimization model and a power grid optimization model, and the interaction between the transportation network optimization model and the power grid optimization model is set to achieve coupling. S4: After the port is repaired, a recovery strategy is gradually obtained based on the transportation network and power grid coupling model to repair the port and obtain the container throughput when the port reaches a stable state after recovery. S5: Calculate the port resilience index based on the container throughput of the port in the initial, minimum, and steady states to optimize port configuration.

[0027] The following is a detailed description of each part: 1. Definition of Port Resilience Index Consider a port infrastructure system whose performance changes due to a sudden disruption event can be achieved through... Figure 1 Modeling is performed. This embodiment introduces a system performance function to describe the port container throughput at time t. The port performance evolves over time through the following three stages: The first stage (before the event occurs), the container throughput is in its initial state. The second phase is when an interruption event occurs. exist When this occurs, due to a decrease in available capacity caused by transportation equipment failure, container throughput begins to decline over time and... Reaching the lowest state at all times The third phase involves repair and maintenance work to improve the capacity of transportation facilities. The recovery process began during this period, and the container throughput during the recovery process is expressed as... until a stable state is reached. .

[0028] Based on this, this embodiment will use the port network's resilience index at time t. Defined as: This index is based on the port network in Recovery state at any moment and The loss during the interruption is presented as a ratio, and the specific expression is as follows: (1) In the formula R is the container throughput of the port system. It should be noted that R here is a dynamically changing value that increases linearly with time during the recovery phase. Initial state Container throughput at any given time Interruption event After it happened The container throughput that always reaches its minimum level. Interruption event Recovery after occurrence Container throughput at any given time Let be the port resilience index of the port network at time t.

[0029] 2. Transportation Network Optimization Model like Figure 2 As shown, the port transport network consists of berths, quay cranes, automated guided vehicles (AGVs), and yard cranes.

[0030] In the network, all links represent planarable transportation routes from the starting point S to the ending point T, and the numbers on the links represent the number of containers that can be transported on that link. The symbols on each node represent the processing capacity of that device. This invention can establish the following optimization model to maximize the throughput of the transportation network: (2) (3) (4) (5) (6) (7) (8) (9) (10) In the formula, the function Used to measure network transport volume. z is a Boolean variable, 1 represents that the resource is selected, and 0 represents the opposite. b, q, a, y represent the maximum processing capacity of a single resource of berth, quay crane, AGV, and yard crane, respectively. x, w, N are the actual capacity from berth to quay crane, quay crane to AGV, and AGV to yard crane. f represents the actual capacity from the starting point to the berth and from the yard crane to the destination. The resources corresponding to these actual capacities can be identified by the subscripts s, i, j, k, m, which represent the starting point, berth number, quay crane number, AGV number, and yard crane number, respectively. The optimization objective of the transport network in equation (2) is to plan the maximum transportable container volume on the path from the starting point S to the destination T. In addition, when containers are transported from the starting point S to the destination T via berth, quay crane, AGV, and yard crane, it is necessary to consider the available capacity of port equipment and the processing capacity of equipment nodes, as reflected in equations (3)-(10). Specifically, constraints (3)-(7) stipulate that the cargo output of each node must not exceed its processing capacity, while constraint (8) indicates that there can only be a single configuration scheme for the number of port facilities. Constraint (9) ensures that the input and output flows remain balanced.

[0031] Transportation network recovery model This embodiment introduces a nonlinear function to simulate the characteristics of various components of the transportation system after an extreme event disruption, as detailed below: (11) In this equation, the subscript "o" represents the set of transportation network components. This indicates the component's original performance (referring to the component's capacity). Indicates the duration of the disruption caused by an extreme event. Indicates that the component is in The ability to recover in a short time, This indicates the remaining capacity of the component after the interruption. Indicates the recovery rate. This is a scaling factor used to indicate the degree of recovery of a transportation network component relative to its original capacity. A nonlinear function (11) captures the core resilience capabilities (absorption and recovery) and the recovery time of the component. This function describes how the component's capacity recovery rate changes over time after a disruptive event.

[0032] Due to the interruption event, the available capacity of each component is reduced during the interruption. The remaining available capacity can be calculated using the following formula: (12) During the recovery process, the capacity recovery status at different time points is monitored via... Progress. For example, the capacity of berth I will recover to its previous level after 6 time periods: .

[0033] 3. Power Network Optimization Model Due to the geographical location of ports and extreme natural disasters such as typhoons, the power distribution components of port power networks are prone to failure, leading to large-scale power outages and system performance degradation. Power network performance can be measured by various indicators, such as the proportion of total load or critical load supplied, the number of power supply units or critical units, the number of available (or faulty) components, and technical indicators such as voltage amplitude and frequency. This invention evaluates microgrid performance based on available load. After an event, the power network will be gradually restored to normal load supply levels through energy system supply and maintenance teams.

[0034] The port power network consists of a microgrid energy system, port electrical equipment, and power supply equipment. This embodiment considers a self-sustaining port microgrid energy system composed of components such as wind turbines, photovoltaic devices, energy storage systems, and diesel generators.

[0035] During the recovery phase, this embodiment assumes that the port energy system operator adopts the following scheduling strategy to ensure efficient energy use: prioritizing the scheduling of wind and photovoltaic power generation to meet the port's load demand, followed by the use of energy storage systems, and finally the activation of distributed power sources.

[0036] The port power network can be modeled as a directed graph. edge set Represents power distribution lines, node set Includes power nodes Exchange node and load nodes (Right now Based on this graphical model, this embodiment establishes the following optimization problem to describe the optimal energy flow in the port microgrid network: (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) In the formula Let (i,j) represent the total load demand, and (i,j) represent the edge from node i to node j in the power network. For power flow, cap is the load limit of the node, and p and d represent the power flow required by the node's generators and electrical equipment, respectively. The operational percentage of node i at time t (may be affected by faults). , , These represent the generator's output at that moment, as well as its lower and upper limits, respectively. , , represents the state of charge of the energy storage device at time t and its lower and upper limits, respectively. As shown in Equation (13), the optimization objective of the power network is to maximize the amount of electricity transmitted. Constraints (14)-(18) describe the power flow relationship between the power source, switching node and load node; constraint (19) ensures that the power flow after the interruption does not exceed the line transmission limit; constraints (23)-(24) ensure that the output of the power generation unit does not exceed its capacity limit.

[0037] Power network recovery model Without loss of generality, this embodiment considers the demand changes of the port energy network under extreme events, such as... Figure 3 As shown.

[0038] Figure 3 The vertical axis represents the system state at different stages. x-axis coordinate This can be understood as a measure of network robustness, that is, the system's ability to withstand extreme events without significant performance degradation. The degree of this performance depends on the component hardening measures taken before the event and the level of proactive management. From that moment on, network performance will continue to degrade until it enters a period of decline. Later stabilized State. At this point, the standardized degradation index (DI) is defined as: (25) Clearly, the index is 0 for a fully functional network, while the DI value is 1 for a completely nonfunctional network.

[0039] from arrive This is the preparation phase for restoring the port's power network. The more resources the port invests in this phase, the more sensitive its response to extreme events will be, and the stronger its recovery capabilities will be. This reflects the extent to which resources have been restored.

[0040] from From that moment on, the power grid officially entered the recovery phase, and... Time to reach Level. The standardized recovery efficiency index (REI) is defined as: (26) This index can be used to assess the efficiency of the recovery phase. A value of 1 indicates that the power grid has been fully restored, while a value of 0 indicates that the recovery has failed.

[0041] 4. Transportation-Power Network Coupling Process This embodiment considers the following two types of interactions between the two networks: (1) The accessibility of damaged nodes in the power grid depends on the road conditions of the transportation network. This interaction primarily affects manual repair work on the power network after an outage. Assuming the maintenance team was located on certain paths of the transportation network before the extreme event, after the outage, they need to reach the damaged nodes via these transportation network roads to carry out repair work, including dismantling collapsed towers, relocating damaged transportation equipment, restoring traffic on affected roads, repairing damaged transportation equipment, repairing distribution lines and towers, and restoring power supply.

[0042] This behavior, influenced by the inherent connectivity of the dual networks, is represented by the following equation: (27) In the formula, For power restoration capability based on the level of the maintenance team, Equation (27) shows that only when maintenance resources are allocated to the damaged node i ( (A Boolean variable indicating whether maintenance resources are allocated to this resource) and the location is accessible. Only when the location is reachable (a Boolean variable) can recovery measures be taken to gradually restore node i to its original functional level; otherwise, node i will remain in a damaged state.

[0043] (2) The operation of the transportation equipment contained in the transportation network requires the power grid to provide energy support. This interaction primarily affects the available capacity of transportation components. When the power demand of transportation components cannot be met, available transportation capacity will be compromised. This intrinsic link is specifically reflected in the ratio of the capacity retention rate to the recovery rate of transportation components.

[0044] From a joint probability perspective, consider the combined impact of outage events and power network failures on the capacity maintenance rate of transport network components: (28) in: This indicates the percentage of capacity that a transportation component can maintain after being affected by an interruption event. This indicates the rate of damage to the transport components themselves caused by extreme events. , The average load availability of the corresponding transportation components in the power grid affected by the interruption can be calculated using the power grid index DI obtained from equation (25), and .

[0045] use This indicates the percentage of remaining component capacity after being affected by the combined impact of an interruption event and a power failure: (29) The restoration of the transportation network takes into account both its own recovery rate and the efficiency of the power grid restoration. The recovery rate of the transport component itself is represented by the index REI obtained from equation (26).

[0046] Joint recovery efficiency By preference parameters (referring to more self-healing or grid recovery). calculate: (30) Port performance based on a collaborative perspective is expressed by equation (31): (31) Disruption scenarios caused by extreme events can be generated using Bayesian networks and Monte Carlo methods. A Bayesian network is a directed acyclic graph where nodes represent random variables and edges represent relationships between parent and child nodes. In a discrete Bayesian network, each node is equipped with a table of conditional probabilities based on the state of its parent node. The product of all conditional probabilities in a Bayesian network constitutes the joint probability distribution of all variables, which takes the following form: (32) Here, For random variables, yes The parent node. In the following discussion, this invention considers two sources of extreme weather events related to the port environment: lightning strikes and gusts of wind. Figure 4 A simple Bayesian network with three nodes is presented, where each node represents a random variable, including lightning strike intensity, wind speed, and the number of failure events. It can be observed that, based on the discrete Bayesian model, the failure level of a component is jointly determined by the combination of wind speed and lightning strike intensity.

[0047] In the power network considered in this invention, tower structures and power lines are vulnerable components susceptible to events, and they can be located at nodes or on connecting lines. The failure rate of a node, influenced by both the tower structure and the power lines, is calculated as follows: (33) in This represents the probability of a substation failing at time t. and These represent the failure probabilities of the power line and the tower structure at time t, respectively.

[0048] The following is an example of the specific processing flow of this solution: S1: Determine system configuration The goal of this step is to model the port transport-power coupled network to be evaluated. This requires specifying the number and location relationships of berths, quay cranes, AGVs, and yard cranes; the nodes of the power system and their location relationships; the locations of wind turbines, photovoltaic systems, energy storage, distributed power sources, and port shore power equipment; and the coupling relationship between the power system and the transport network. This use case uses... Figure 5 The network shown.

[0049] S2: Determine relevant parameters This step requires specifying the combined wind and solar power output curve. Then, based on the combined impact of wind and lightning, different event levels are set, and the average number of overhead power line failures at each level is derived. Next, based on the probability distributions of the set wind and lightning events at different levels, the failure probability of the power distribution lines at each event level is calculated through joint probability calculation. Finally, the time when the port is affected by extreme events is set, with both grids affected, and the event level at each moment is simulated. The combined wind and solar power output curve in this use case is as follows: Figure 6 As shown. The failure rates of lines and towers are as follows: Figure 7 As shown. The relationship between the extreme event level and time is as follows. Figure 8 As shown in the figure. The relationship between the load demand of the power network and time is as follows. Figure 9 As shown.

[0050] S3: Conduct a toughness assessment S301: Examine the network status before the interruption. A maximum flow algorithm based on node capacity constraints is used to calculate the transport volume under the initial capacity of each link. The size of the containers to be transported from the origin S is set to obtain the maximum flow transport path planning before extreme events. Next, the number of berths required for this path is calculated, and based on the processing capacity of each transport device on the obtained maximum flow transport path, the original network's maximum transport capacity is obtained. Finally, the substation power supply capacity constraint under the original state is set.

[0051] This use case sets the transport capacity at 1250 TEUs. The optimal route obtained after maximum flow path planning is S-B3-Q14-A64-Y12-T. The handling capacity of the three berths far exceeds 1250 TEUs, but the handling capacity of 28 quay cranes is 560 TEUs, and the handling capacity of 145 AGVs is 512 TEUs. Although the handling capacity of 12 yard cranes could reach 560 TEUs, the actual maximum transport capacity is 512 TEUs due to the limitations of AGVs. The port's power grid load requirement is 3110 kW, which will be met by the substation and wind and solar generators.

[0052] S302: Examining the Degradation Stage The formula for calculating the functional level of a power supply node (substation) is as follows: This step requires starting from time zero and gradually calculating the operating status of the coupled network at each time. Specifically, at each time, the output of the substation needs to be calculated first. Based on the comparison between the output of the substation and the load demand, the operating status of the energy storage system is obtained. When the load demand can be met, the energy storage continues to charge; otherwise, it discharges. When the energy storage still cannot meet the demand after discharging, the distributed power source is activated. If the gap still cannot be filled, the grid begins to degrade. The degradation index DI is calculated according to equation (25).

[0053] The degradation of the road network is calculated only when the grid load reaches its lowest point (i.e., when the impact is most severe). This is because extreme events directly affect the grid, and the focus on the road network is limited to its minimum transport capacity for subsequent resilience assessment. Therefore, it is not necessary to calculate the road network status at every moment during the decline phase. The failure rate of transport components is obtained based on the simulated failure characteristics. The remaining processing capacity of all types of components is calculated according to equations (29) and (31). Based on this, the maximum flow transport path is recalculated and its transport capacity is recorded.

[0054] In this use case, the failure rate caused by extreme events reaches its peak at t=4. At t=1, the substation output drops to 2114.95kW, and each wind and solar unit can supply 300kW, which can still meet the load demand. The surplus electricity is stored in the energy storage system. At t=2, the substation output drops to 1700.02kW, the wind and solar power supply is insufficient, the energy storage begins to discharge, but there is still a load gap of about 200kW, and the grid begins to degrade. At t=4, the substation failure is the most serious, and the 945kW load cannot be met. At t=7, the grid load drops to the lowest point of 1842.65kW, the wind and solar output is less than 200kW, and the distributed generator sets start to supply power at 90kW / hour. According to formula (25), the degradation index DI=0.2335 is calculated.

[0055] The failure rate parameters of transportation components affected by extreme events are: 0.30, 0.62, 0.55, 0.5.

[0056] At this time, the transportation network is also affected. According to equation (29), the retention capacity ratio of the components is calculated as: 0.52, 0.28, 0.33, 0.37.

[0057] Taking the calculation of available capacity of berth B as an example: This reduces the number of available berths from 4 to 2. The capacity of other components is similar.

[0058] At this point, the maximum container flow path planning for the transportation network is: B2-Q4-A20-Y4-T Therefore, the maximum transport capacity of the network in the degradation phase is =160 TEU.

[0059] S303: Assessment of the Recovery Phase The power grid restoration process is summarized as follows: After an incident, a professional maintenance team immediately begins repair work on the damaged power grid. However, due to the collateral impact on the transportation network, these personnel must first complete road clearing tasks, including moving damaged transportation equipment blocking routes and clearing fallen power lines, in order to restore road traffic capacity and repair routes.

[0060] Only after completing the initial tasks can the maintenance team begin repairing faulty power lines and power supply nodes, gradually restoring power to the disaster-stricken area.

[0061] This step requires first setting the hourly power supply capacity recovery efficiency and configuring the recovery parameters. A repair observation time point is set to assess resilience. From this time point to the observation time point, the latest power supply node flow limit is calculated hourly. When the observation time point arrives, REI is calculated according to Equation (26) based on the grid load recovery status at that time. Then, the recovery efficiency r of each type of component is calculated at this time, the joint component recovery efficiency is calculated according to Equation (30), the expected recovery efficiency is set, the post-recovery processing capacity of all components is calculated, and the maximum flow transport path is recalculated and its transport capacity is recorded based on this.

[0062] This use case assumes the maintenance team can restore overall power capacity at an efficiency of 6% per hour. The repair process begins at time 2, with resources being allocated and a connection established at time 6. Taking time 13 as the starting point, the capacity recovery of the switching node containing the wind and solar generators during the period t=7-13 is as follows: 120, 125, 130, 140, 150, 170, 180. By time t=13 (6 hours after the start of restoration), the grid load has recovered to 2577.61 kW. The recovery efficiency index (REI) is 0.2595.

[0063] The recovery efficiencies of various types of transportation components under extreme events are: 0.84, 0.79, 0.87, and 0.78.

[0064] Considering the recovery of grid capacity, the combined component recovery rate ratios are calculated using equation (30): 0.72, 0.68, 0.74, 0.67.

[0065] Assuming the expected recovery efficiency of all components =0.9. After 6 hours of restoration work on the equipment at each node of the transportation network, the following can be obtained using berth B: This means that the available berth capacity has been restored to 3. The capacities of other components are restored in the same manner. At this point, the maximum planned container flow path in the transport network is: S-B3-Q13-A59-Y11-T Therefore, the maximum network capacity after 6 hours of recovery is: S304: Obtain the port toughness calculation results The port resilience calculation results are obtained based on equation (1).

[0066] The port resilience calculation results after 6 hours of recovery in this use case are as follows: Port resilience curves that take into account both power and transportation networks, such as Figure 10 As shown.

[0067] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A port resilience optimization method based on a transportation network and power grid coupling model, characterized in that, include: Obtain the system configuration and structural parameters of the transportation and power networks of the port to be evaluated; Calculate the container throughput of the port in its initial state before extreme events occur; After an extreme event occurs at the port, based on a pre-established coupled model of the transportation network and the power grid, the operating status of the coupled network consisting of the transportation network and the power grid is calculated step by step at each time point, and the minimum container throughput of the port is calculated. After the port is repaired, a recovery strategy is gradually obtained based on the coupling model of the transportation network and the power grid to repair the port and obtain the container throughput when the port reaches a stable state after recovery. The port resilience index is calculated based on the container throughput of the port in its initial, minimum, and steady states to optimize port configuration.

2. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 1, characterized in that, The formula for calculating the port resilience index is as follows: In the formula, Initial state Container throughput at any given time Interruption event After it happened The container throughput that always reaches its minimum level. Interruption event Recovery after occurrence Container throughput at any given time Let be the port resilience index of the port network at time t.

3. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 1, characterized in that, The transportation network and power grid coupling model includes a transportation network optimization model and a power network optimization model, and sets up the interaction between the transportation network optimization model and the power network optimization model to achieve coupling; The interactions include: the accessibility of damaged nodes in the power grid depends on the road conditions of the transportation network, and the functional operation of the transportation equipment contained in the transportation network requires energy support from the power grid.

4. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 3, characterized in that, The transportation network optimization model aims to optimize the maximum number of containers that can be transported along the path from the starting point S to the ending point T. The corresponding constraints include the constraint that the cargo output of each node must not exceed its processing capacity, the constraint that the number of port equipment can only have a single configuration scheme, and the constraint that the input and output flow must be kept in balance. The power network optimization model takes maximizing the amount of electricity transmitted as the constraint objective, and the corresponding constraints include the power flow relationship constraints between power sources, switching nodes, and load nodes. The constraints include ensuring that the power flow after an interruption does not exceed the line transmission limit, and ensuring that the output of the power generation unit does not exceed its capacity limit.

5. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 3, characterized in that, During the degradation phase following an extreme event at the port, the method calculates the substation output of the power network at each moment. If the substation output meets the corresponding load demand, the power network's energy storage system continues to charge; otherwise, the energy storage system discharges. If the energy storage system's discharge still cannot meet the corresponding load demand, then the distributed power generation of the power grid is activated. If the distributed power generation also cannot meet the corresponding load demand, then the power grid begins to degrade. When the load of the power grid reaches its lowest point, the corresponding degradation index is calculated, and the remaining available capacity of all types of components is calculated based on the failure rate of the transportation components. Based on this, the scheduling optimization is carried out again through the transportation network and power grid coupling model to determine the corresponding maximum container throughput.

6. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 5, characterized in that, The formula for calculating the degradation index is as follows: In the formula, The degradation index, The moment when degradation begins. For the stable moment after degradation, This represents the load requirements in the initial state. for t Current load requirements; The formula for calculating the remaining available capacity is: In the formula, This represents the percentage of remaining component capacity after being affected by a combined interruption event and power failure. The damage rate of transportation components due to extreme events; the subscript 'o' represents the set of transportation network components. This is a proportionality coefficient used to indicate the degree of recovery of transportation network components relative to their original capacity. The original capacity of the component. To improve efficiency, The duration of the disruption caused by extreme events. For components in The ability to recover in a short time.

7. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 3, characterized in that, The method calculates the corresponding standardized recovery efficiency index based on the power grid load recovery status at each moment after the port repair work is carried out, thereby calculating the joint recovery efficiency to determine the remaining available capacity of all types of components. Based on this, the method re-optimizes the scheduling through the transportation network and power grid coupling model to determine the corresponding maximum container throughput.

8. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 7, characterized in that, The formula for calculating the standardized recovery efficiency index is as follows: In the formula, To standardize the recovery efficiency index, This is a point in time when the power grid has been restored. This marks the moment when the power grid officially enters the recovery phase. This represents the load requirements in the initial state. For the load demand in the steady state after power grid degradation, for t Current load requirements; The formula for calculating the combined recovery efficiency is: In the formula, To improve overall recovery efficiency, the subscript 'o' represents a set of transportation network components. For preference parameters, For the recovery rate of the transport components themselves; The formula for calculating the remaining available capacity is: In the formula, This represents the percentage of remaining component capacity after being affected by a combined interruption event and power failure. The damage rate of the transport components themselves due to extreme events. To indicate the expected recovery efficiency of a component, This is the original capacity of the component. The duration of the disruption caused by extreme events. For components in The ability to recover in a short time.

9. The port resilience optimization method based on a transportation network and power grid coupling model according to claim 8, characterized in that, The method involves setting the expected recovery efficiency for all components. and recovery time The optimal scheduling scheme and the corresponding maximum container throughput of the coupled transportation network and power grid model are obtained, thereby determining the corresponding port resilience index.

10. A port resilience optimization system that implements the port resilience optimization method based on a transportation network and power grid coupling model as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire the system configuration and structural parameters of the transportation network and power network of the port to be evaluated; The port initial state calculation module is used to calculate the container throughput of the port in its initial state before extreme events occur. The port minimum state calculation module is used to calculate the operating state of the coupled network consisting of the transportation network and the power grid at each time step by step, based on a pre-established transportation network and power grid coupling model, after an extreme event occurs at the port, and to calculate the container throughput of the port in the minimum state. The port recovery status calculation module is used to gradually obtain recovery strategies based on the transportation network and power grid coupling model after port repair work is carried out, so as to repair the port and obtain the container throughput when the port reaches a stable state after recovery. The port resilience optimization module is used to calculate the port resilience index based on the container throughput of the port in the initial, minimum, and steady states, in order to optimize the port configuration.

Images