A power distribution network emergency resource allocation method and device for rainstorm disasters

Abstract

The application relates to a rainstorm disaster-oriented power distribution network emergency resource allocation method and device. The method comprises the following steps: obtaining rainstorm evolution data of a target area; determining water accumulation change data of each catchment area in the target area according to the rainstorm evolution data; determining fragile branches and non-fragile branches in the power distribution network according to the water accumulation change data of each catchment area; taking the number of faults in the fragile branches and the non-fragile branches and the emergency resource access constraint as constraint conditions, solving a preset target function to obtain an emergency resource allocation scheme, and the emergency resource allocation scheme comprises preset positions of emergency resources and emergency strategies; and the target function is used for maximizing the power supply of the power distribution network. Through the application, the response speed can be improved, the power outage duration can be shortened, and the operation resilience and resource allocation efficiency of the power distribution network under rainstorm waterlogging disasters can be improved.

Description

Technical Field

[0001] This application relates to the field of power distribution network resource optimization technology, and in particular to an emergency resource allocation method for power distribution networks in response to rainstorm disasters. Background Technology

[0002] With the overall trend of increasing extreme heavy precipitation events, the climate risks faced by urban infrastructure are constantly increasing. Extreme heavy precipitation events have become one of the important factors threatening the safe operation of cities. Correspondingly, the prediction and defense of urban power risks under extreme heavy precipitation weather conditions have become an important issue for the safe operation of the power system.

[0003] In related technologies, emergency response models typically involve repairs and resource allocation only after a disaster has occurred, resulting in problems such as delayed response, low resource allocation efficiency, and high rescue costs. Summary of the Invention

[0004] Therefore, it is necessary to provide a power distribution network emergency resource allocation method for rainstorm disasters to address the aforementioned technical issues.

[0005] Firstly, this application provides a method for emergency resource allocation in power distribution networks for rainstorm disasters, including:

[0006] Acquire data on the evolution of heavy rainfall in the target area;

[0007] Based on the data on the evolution of the rainstorm, determine the changes in water accumulation in each catchment area within the target region;

[0008] Based on the data on water volume changes in each catchment area, vulnerable and non-vulnerable branches in the distribution network are identified;

[0009] Using the fault number boundary in vulnerable and non-vulnerable branches and the emergency resource access constraint as constraints, the preset objective function is solved to obtain the emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network.

[0010] In one embodiment, acquiring rainfall evolution data for the target area includes:

[0011] Obtain the formula for expressing rainfall intensity; the formula for expressing rainfall intensity includes the correspondence between rainfall recurrence period, rainfall duration, and rainfall parameters;

[0012] Based on the obtained rain peak location coefficient and rainstorm intensity expression formula, rainstorm evolution data is calculated, which includes pre-peak data and post-peak data.

[0013] In one embodiment, determining the changes in water accumulation in each catchment area within the target region based on rainfall evolution data includes:

[0014] Multiple catchment areas were obtained by dividing the target area based on the differences in elevation information;

[0015] For each catchment area, the change in water accumulation is determined based on the rainfall, drainage, and the flow between the catchment area and adjacent catchment areas.

[0016] In one embodiment, vulnerable and non-vulnerable branches in the distribution network are determined based on changes in water accumulation in each catchment area, including:

[0017] Determine the geographical location of each branch in the power distribution network;

[0018] For each branch road, the geographical location is mapped to each catchment area to determine the catchment area to which the branch road belongs;

[0019] If the water volume in the catchment area to which the branch belongs is detected to be greater than or equal to the preset water volume threshold, the branch is determined to be a vulnerable branch.

[0020] If the water volume in the catchment area to which the branch belongs is less than the water volume threshold, the branch is determined to be a non-vulnerable branch.

[0021] In one embodiment, the emergency resource allocation scheme is obtained by solving a preset objective function, using the fault number boundary between vulnerable and non-vulnerable branches and a preset balance of emergency resource power generation as constraints, to obtain the emergency resource allocation scheme, including:

[0022] Obtain the preset optimal tolerance, select any feasible candidate resource allocation scheme as the initial solution, and solve the sub-problem based on the fault number boundary in the vulnerable and non-vulnerable branches and the preset emergency resource power generation balance as constraints and candidate resource allocation schemes to obtain the fault scenario that minimizes the objective function value, and use the corresponding objective function value as the lower bound score of the candidate resource allocation scheme.

[0023] The fault scenario that minimizes the objective function value is added as a new constraint to the main problem. The main problem is solved to obtain the optimal allocation scheme. The objective function value corresponding to the optimal allocation scheme is calculated to obtain the upper bound score of the optimal allocation scheme. The main problem is used to formulate emergency allocation schemes, and the sub-problems are used to evaluate fault scenarios.

[0024] Based on the lower bound score, the upper bound score, and the optimal tolerance, an emergency resource allocation plan is determined.

[0025] In one embodiment, an emergency resource allocation plan is determined based on a lower bound score, an upper bound score, and an optimal tolerance, including:

[0026] The calculation ends when the difference between the upper and lower bound scores is less than the optimal tolerance, and an emergency resource allocation plan is obtained based on the optimized allocation plan.

[0027] In one embodiment, an emergency resource allocation plan is determined based on a lower bound score, an upper bound score, and an optimal tolerance, including:

[0028] If the difference between the upper bound score and the lower bound score is detected to be greater than or equal to the optimal tolerance, the sub-problem is solved according to the optimized allocation scheme to obtain the intermediate fault scenario that minimizes the objective function value, and the updated lower bound score is obtained according to the objective function and the current lower bound score.

[0029] The intermediate failure scenario is added as a new constraint to the main problem. The intermediate resource allocation scheme is obtained by solving the main problem. The corresponding objective function value is calculated based on the intermediate resource allocation scheme to obtain the updated upper bound score.

[0030] Return to the execution step of determining the emergency resource allocation plan based on the lower bound score, upper bound score, and optimal tolerance.

[0031] Secondly, this application also provides an emergency resource allocation device for power distribution networks in response to rainstorm disasters, comprising:

[0032] The acquisition module is used to acquire data on the evolution of heavy rainfall in the target area;

[0033] The water accumulation calculation module is used to determine the changes in water accumulation in each catchment area of ​​the target region based on the rainstorm evolution data;

[0034] The determination module is used to identify vulnerable and non-vulnerable branches in the distribution network based on the data on changes in water accumulation in each catchment area.

[0035] The generation module is used to solve a preset objective function with the fault number boundary between vulnerable and non-vulnerable branches and the preset balance of emergency resource generation power as constraints, to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] Acquire data on the evolution of heavy rainfall in the target area;

[0038] Based on the data on the evolution of the rainstorm, determine the changes in water accumulation in each catchment area within the target region;

[0039] Based on the data on water volume changes in each catchment area, vulnerable and non-vulnerable branches in the distribution network are identified;

[0040] Using the fault number boundary in vulnerable and non-vulnerable branches and the emergency resource access constraint as constraints, the preset objective function is solved to obtain the emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Acquire data on the evolution of heavy rainfall in the target area;

[0043] Based on the data on the evolution of the rainstorm, determine the changes in water accumulation in each catchment area within the target region;

[0044] Based on the data on water volume changes in each catchment area, vulnerable and non-vulnerable branches in the distribution network are identified;

[0045] Using the fault number boundary in vulnerable and non-vulnerable branches and the emergency resource access constraint as constraints, the preset objective function is solved to obtain the emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network.

[0046] The above-mentioned method for emergency resource allocation in power distribution networks in response to rainstorm disasters acquires rainstorm evolution data for the target area;

[0047] Based on the data on the evolution of the rainstorm, determine the changes in water accumulation in each catchment area within the target region;

[0048] Based on the data on water volume changes in each catchment area, vulnerable and non-vulnerable branches in the distribution network are identified;

[0049] Using the fault number boundary between vulnerable and non-vulnerable branches and the emergency resource access constraint as constraints, a preset objective function is solved to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network. Through the embodiments of this application, the rainstorm evolution process, emergency resource pre-positioning, and distribution network resource allocation emergency strategies are combined. On the one hand, the vulnerability identification results of branches and nodes can be obtained for rainstorms, making the pre-positioning of emergency resources more targeted. On the other hand, the combination of emergency resource pre-positioning and distribution network reconfiguration takes into account the actual topological changes of the distribution network after a disaster, thereby improving the power supply guarantee capability of critical loads, shortening the duration of power outages, and improving the operational resilience and emergency response efficiency of the distribution network under rainstorm and flood disasters. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is an application environment diagram of the distribution network emergency resource allocation method in one embodiment;

[0052] Figure 2 This is a flowchart illustrating a distribution network emergency resource allocation method in one embodiment;

[0053] Figure 3 This is a schematic diagram of the process for obtaining rainstorm evolution data in one embodiment;

[0054] Figure 4 This is a flowchart illustrating the calculation of changes in water accumulation in one embodiment;

[0055] Figure 5 This is a flowchart illustrating the process of obtaining an emergency resource allocation plan in one embodiment;

[0056] Figure 6 This is a flowchart illustrating the process of determining an emergency resource allocation plan in one embodiment;

[0057] Figure 7 This is a schematic diagram of a rainfall process in one embodiment;

[0058] Figure 8 This is a schematic diagram of urban flooding in one embodiment;

[0059] Figure 9 This is a schematic diagram of a node testing system in one embodiment;

[0060] Figure 10 This is a schematic diagram of the model convergence curve in one embodiment;

[0061] Figure 11 This is a schematic diagram comparing resource pre-provisioning and non-resource pre-provisioning in one embodiment;

[0062] Figure 12 This is a structural block diagram of a power distribution network emergency resource allocation device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] In recent years, extreme heavy rainfall events have generally been on the rise, increasing the climate risks faced by urban infrastructure. Rainstorm disasters, due to their suddenness, wide impact, and long chain of consequences, have become a significant factor threatening urban safety. Research on the prediction and prevention of urban power risks under extreme rainstorm conditions has become an important topic in the safe operation of power systems and the construction of resilient urban distribution networks.

[0065] In related technologies, emergency response models typically involve repairs and resource allocation only after a disaster occurs, resulting in problems such as delayed response, low resource allocation efficiency, and high rescue costs. Especially in cases of severe urban flooding and road blockages, post-disaster dispatching struggles to quickly meet the power supply needs of affected areas. Therefore, research on the pre-disaster deployment of emergency resources in power distribution networks is of great significance. By modeling disaster sites based on rainstorm disasters and urban flooding risks, potentially affected areas can be predicted before a disaster, and emergency resources can be optimally allocated. This method enables the rational allocation of resources before rainstorms occur, improving repair efficiency, shortening power outage recovery time, and effectively enhancing the resilience and disaster resistance of urban power systems.

[0066] In summary, the above description illustrates the main problems existing in the relevant technologies: emergency response models usually carry out repairs and resource allocation only after a disaster occurs, resulting in problems such as delayed response, low resource allocation efficiency, and high rescue costs.

[0067] Based on this, this application provides a method for emergency resource allocation in a distribution network for rainstorm disasters. The method includes: acquiring rainstorm evolution data for a target area; determining the water volume change data of each catchment area in the target area based on the rainstorm evolution data; determining vulnerable and non-vulnerable branches in the distribution network based on the water volume change data of each catchment area; solving a preset objective function using the fault number boundaries of vulnerable and non-vulnerable branches, the power grid topology, and emergency resource access constraints as constraints to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset locations of emergency resources and emergency strategies. The objective function is used to maximize the power supply of the distribution network. Through this application, limited emergency resources can be deployed to suitable node locations before a disaster occurs, and after a disaster, in conjunction with the distribution network reconfiguration strategy, rapid recovery and power supply path optimization can be achieved, thereby effectively improving the power supply guarantee capability of critical loads, shortening the duration of power outages, and improving the operational resilience and emergency response efficiency of the distribution network under rainstorm and flood disasters. See the following embodiments for details.

[0068] The emergency resource allocation method for power distribution networks in response to rainstorm disasters provided in this application can be applied to, for example... Figure 1 In the application environment shown, the data acquisition terminal 102 communicates with the server 104 via a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104 or placed on the cloud or other network servers. The process involves acquiring rainstorm evolution data for the target area; determining the water volume change data of each catchment area in the target area based on the rainstorm evolution data; determining vulnerable and non-vulnerable branches in the distribution network based on the water volume change data of each catchment area; solving a preset objective function using the fault number boundary of vulnerable and non-vulnerable branches, the power grid topology, and emergency resource access constraints as constraints to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset locations of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network. The data acquisition terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Headset devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0069] In one exemplary embodiment, such as Figure 2 As shown, a method for emergency resource allocation in power distribution networks for rainstorm disasters is provided, and this method is applied to... Figure 1 Taking the server in the example of this, the explanation includes:

[0070] S210: Obtain data on the evolution of heavy rainfall in the target area.

[0071] Among them, the rainstorm evolution data refers to the rainfall intensity at each moment in the target area during a complete rainfall process. The target area is a pre-defined region, which can be a real city or a pre-defined, virtual region.

[0072] In this embodiment of the application, the rainstorm intensity formula of the target area can be obtained through the rainstorm statistics released by the natural resources and planning bureau to which the target area belongs. Considering that the actual rainstorm is not evenly distributed over the total duration and has a single-peak characteristic, the average intensity rainstorm intensity formula can be further converted into a minute-by-minute intensity sequence to obtain the above-mentioned rainstorm evolution data.

[0073] S220 determines the changes in water accumulation in each catchment area within the target region based on the evolution data of the rainstorm.

[0074] A catchment area is a unit defined based on surface elevation information. The basic principle of catchment area division is that water, under the influence of gravity, tends to flow from high to low elevations. Each region has different slope information, and the elevation information of its apex also varies. Therefore, catchment areas can be divided based on elevation differences in different geographical locations. In short, areas that flow to the same depression constitute a catchment area.

[0075] Water volume change data reflects the changes in water volume at different times within each catchment area. As can be understood, during a rainstorm, the water volume within a catchment area is dynamically changing. Rainfall continuously enters, while some water infiltrates into the soil through the surface, some is discharged through drainage networks, and some overflows into adjacent, lower-lying catchment areas. Water volume change data tracks this dynamic inflow and outflow to calculate the water volume at each time point throughout the entire rainstorm process in each catchment area.

[0076] In this embodiment of the application, the change data of water accumulation in each catchment area at each time can be obtained based on data such as rainfall in the catchment area, surface infiltration rainfall, flow rate from the catchment area to adjacent areas, flow rate from adjacent areas into the catchment area, and drainage volume of the catchment area's drainage pipes.

[0077] S230, based on the data on changes in water accumulation in each catchment area, determines the vulnerable and non-vulnerable branches in the distribution network.

[0078] Vulnerable branch roads are those located in flood-prone areas, meaning they are likely to break due to waterlogging during heavy rain. Similarly, non-vulnerable branch roads are those located in higher or better-drained areas that are not prone to flooding.

[0079] In this application, given the geographical location of each branch road, spatial analysis is performed on the geographical location of each branch road and the water accumulation data of each catchment area at different times. This allows us to determine the water accumulation of each branch road's catchment area at each time point during the entire rainstorm process. Similarly, the same method can be used to determine whether each node in the distribution network is a vulnerable node or a non-vulnerable node.

[0080] S240 uses the fault number boundary in vulnerable and non-vulnerable branches, the power grid topology, and emergency resource access constraints as constraints to solve a preset objective function, resulting in an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies. The objective function is used to maximize the power supply of the distribution network.

[0081] Among them, the fault number boundary between vulnerable branches and non-vulnerable branches refers to the maximum number of faults allowed to occur in vulnerable branches and non-vulnerable branches, respectively. Since vulnerable branches are located in flood-prone areas, their maximum number of faults limits the upper limit of the number of faults, preventing the unreasonable extreme assumption that all vulnerable lines will fail in subsequent optimization. Similarly, the maximum number of faults allowed to occur in non-vulnerable branches is also limited.

[0082] The power grid topology refers to the electrical connection relationship between nodes in a distribution network. The topology can be changed by remotely controlling the switches on the lines. For example, opening the switch means cutting off the connection between the corresponding nodes, and closing the switch means restoring the corresponding connection. The above emergency strategy includes how to decide whether to open or close the switches of each line.

[0083] The aforementioned emergency resource access constraints include the maximum number of emergency resources a node is allowed to access, the requirement that each emergency resource can only be accessed by one node, and the preset number of emergency resources.

[0084] The aforementioned emergency resource allocation plan is the final output of this project. It includes at least the pre-positioning locations of emergency resources, i.e., at which nodes effective emergency resources such as mobile generators and drainage pumps will be deployed before the arrival of heavy rain. The emergency resource allocation plan also includes emergency strategies, i.e., which branches should be disconnected / connected to adjust the power grid topology when a disaster occurs.

[0085] In this embodiment, an objective function is first constructed, employing a three-tiered hierarchical decision-making architecture. The first-tier decision variables include the location of pre-set emergency resource nodes and the on / off status of each branch. The second-tier decision uses the fault states of vulnerable and non-vulnerable branches as uncertain variables. The third-tier decision variables include the actual active and reactive power provided by each node, representing the power supply scheduling based on the available output of pre-set resources and the grid connection path after a fault occurs. The optimization objective of this objective function is to maximize the worst-case weighted sum of the survivability load during a disaster.

[0086] After obtaining the objective function, set constraints to ensure that the solution is actually feasible. The constraints should include at least the fault number boundary in vulnerable and non-vulnerable branches, the power grid topology, and the emergency resource access constraints mentioned above.

[0087] Finally, the column-and-constraint generation (C&CG) algorithm is used to complete the iterative solution, thereby obtaining the above emergency resource allocation scheme.

[0088] This application combines the rainstorm evolution process, emergency resource pre-positioning, and distribution network resource allocation emergency strategies. On the one hand, it can identify the vulnerability of branches and nodes in response to rainstorms, making the pre-positioning of emergency resources more targeted. On the other hand, it combines emergency resource pre-positioning with distribution network reconfiguration, taking into account the actual topological changes of the distribution network after a disaster. This can improve the power supply guarantee capability of critical loads, shorten the duration of power outages, and enhance the operational resilience and emergency response efficiency of the distribution network under rainstorm and flood disasters.

[0089] In one exemplary embodiment, such as Figure 3 As shown, the data on the evolution of heavy rainfall in the target area is obtained, including:

[0090] S310, obtain the formula for expressing rainstorm intensity; the formula for expressing rainstorm intensity includes the correspondence between rainstorm return period, rainfall duration and rainfall parameters.

[0091] The formula for expressing the intensity of a rainstorm is:

[0092]

[0093] Where q is the intensity of the rainstorm, P is the return period of the rainstorm (in years), t is the duration of rainfall (in minutes), A is the rainfall intensity parameter, C is the rainfall intensity variation parameter, b is the rainfall duration correction parameter, and n is the rainstorm attenuation index.

[0094] The formula for expressing the intensity of heavy rainfall is a statistical formula published by the natural resources and planning bureau of the target area, which can reflect the relationship between the intensity of heavy rainfall in the area and different return periods and different rainfall durations.

[0095] S320, based on the obtained rain peak location coefficient and rainstorm intensity expression formula, calculates rainstorm evolution data, which includes pre-peak data and post-peak data.

[0096] Considering that the actual rainfall is not evenly distributed over the total duration and exhibits a unimodal characteristic, the Chicago rainfall pattern, which has a unimodal nature, can be selected based on the above rainfall intensity formula to design the rainfall process, thereby obtaining the rainfall intensity at each time point during the rainfall evolution, i.e., the above-mentioned rainfall evolution data, which is specifically represented as follows:

[0097]

[0098]

[0099] Where i1 is the instantaneous rainfall intensity before the peak (i.e., the above-mentioned pre-peak data) (unit: mm·min) -1 i2 represents the instantaneous rainfall intensity after the peak (i.e., the post-peak data mentioned above) (unit: mm·min). -1), t1 is the time before the peak (in minutes), t2 is the time after the peak (in minutes), and r is the rain peak position coefficient.

[0100] Through the embodiments of this application, data on the evolution of rainstorms can be obtained.

[0101] In one exemplary embodiment, such as Figure 4 As shown, the changes in water accumulation in each catchment area within the target region are determined based on the rainfall evolution data, including:

[0102] S410, based on the differences in elevation information of the target area, divides the area into multiple catchment zones;

[0103] S420, for each catchment area, the change in water accumulation in the catchment area is determined based on the rainfall, drainage, and the flow between the catchment area and adjacent catchment areas.

[0104] In this application, the aforementioned catchment area is commonly triangular in shape.

[0105] It is important to first clarify that rainfall runoff refers to the water flow that converges along the surface and underground towards rivers, aquifers, and other water bodies. The formation of rainfall runoff includes two sub-processes: runoff generation and runoff confluence. Runoff generation is the redistribution of rainwater by the underlying surface; the net rainfall volume formed after deducting losses such as soil absorption is the runoff volume. The runoff confluence process refers to the outflow of rainfall exceeding the surface storage capacity of a region to adjacent areas; the rainfall volume that converges between regions is the runoff volume.

[0106] The basic principle of catchment area delineation is that water, under the influence of gravity, tends to flow from high to low elevations. In a Triangulated Irregular Network (TIN), each triangle has a different slope and varying elevations at its vertices. Therefore, catchment areas can be delineated based on the elevation differences between the triangle nodes. In short, water within a triangle flows from the lowest point of its vertices to adjacent triangles, until it reaches the lowest point (depression) within the region. Triangles flowing to the same depression within the region constitute a catchment area.

[0107] According to the law of conservation of mass, the water volume in the catchment area during urban flooding can be obtained based on the runoff generation and distribution of the catchment area from the rainfall data calculated above. Considering factors such as rainfall, surface infiltration, surface runoff, and pipeline flow, the formula for calculating the water volume in the catchment area is as follows:

[0108]

[0109]

[0110] Among them, Q fiRainfall in catchment area i (in meters) 3 ), Q pi The surface infiltration rainfall in catchment area i (in m³) 3 ), Q i_in The flow rate from catchment area i to adjacent areas (in m³) 3 ), Q i_out The inflow rate (in m³) from adjacent areas into catchment area i 3 ), Q di Rainfall in the drainage pipes of catchment area i (in meters) 3 ), This represents the surface infiltration coefficient.

[0111] In the formula for calculating water accumulation, rainfall can be simulated using the calculated storm data. Surface infiltration rainfall can be obtained by determining the infiltration coefficient based on the properties of the underlying surface in the study area and then further solving for it. Rainfall through drainage pipes needs to be calculated based on knowledge of the drainage network distribution in the study area; therefore, the drainage capacity is set as a constant value for ease of calculation. The catchment volume of different catchment areas can be calculated based on the lowest point (outlet) at the boundary between the catchment area and adjacent catchment areas and the water surface elevation. The specific calculation formula is as follows:

[0112]

[0113] Where A is the area of ​​the cross-section of the water accumulation at the outlet (in m²). 2 α is the slope velocity constant (unit: m·s). -1 ), t is the time step, and S is the average slope of the catchment area.

[0114] In summary, the change in water volume Q in each catchment area can be obtained. i .

[0115] In one exemplary embodiment, vulnerable and non-vulnerable branches in the distribution network are determined based on the changes in water accumulation in each catchment area, including:

[0116] Determine the geographical location of each branch in the power distribution network;

[0117] For each branch road, the geographical location is mapped to each catchment area to determine the catchment area to which the branch road belongs;

[0118] If the water volume in the catchment area to which the branch belongs is detected to be greater than or equal to the preset water volume threshold, the branch is determined to be a vulnerable branch.

[0119] If the water volume in the catchment area to which the branch belongs is less than the water volume threshold, the branch is determined to be a non-vulnerable branch.

[0120] In this embodiment, spatial data of each branch in the power distribution network is first obtained, and the spatial data is then uniformly converted into the same projected coordinate system as the water accumulation calculation model.

[0121] Furthermore, for each branch road, based on its geographical location data, the geographical location of the branch road is spatially intersected with the spatial boundary data of the previously divided catchment areas. For each branch road, based on the spatial range it traverses, its geometric location is determined to be within which catchment area(s).

[0122] If the geometric center of a branch falls within a certain catchment area, then that catchment area is determined to be the catchment area to which the branch belongs. At this time, if the water volume in the catchment area is detected to be greater than or equal to the water volume threshold, the branch is determined to be a vulnerable branch. If the water volume in the catchment area to which the branch belongs is detected to be less than the water volume threshold, the branch is determined to be a non-vulnerable branch.

[0123] If a branch road crosses multiple catchment areas, it is considered a vulnerable branch if the water volume in any one of these catchment areas is greater than or equal to a water volume threshold. If the water volume in all the catchment areas crossed by the branch road is less than the water volume threshold, the branch road is considered a non-vulnerable branch. The water volume threshold can be set by relevant technical personnel according to actual needs.

[0124] This application determines vulnerable and non-vulnerable branches based on the spatial correspondence between the geographical location of the branch and the catchment area, rather than relying on manual experience to delineate risk areas or on coarse administrative grids to divide risks. This avoids false alarms or omissions caused by misjudgment of location and ensures that the determination of each branch has clear spatial data support.

[0125] In one exemplary embodiment, such as Figure 5 As shown, using the fault number boundary between vulnerable and non-vulnerable branches and the preset balance of emergency resource generation power as constraints, the preset objective function is solved to obtain the emergency resource allocation scheme, including:

[0126] S510: Obtain the preset optimal tolerance, select any feasible candidate resource allocation scheme as the initial solution, and solve the subproblem based on the fault number boundary in the vulnerable and non-vulnerable branches and the preset emergency resource power generation balance as constraints and candidate resource allocation schemes to obtain the fault scenario that minimizes the objective function value, and use the corresponding objective function value as the lower bound score of the candidate resource allocation scheme.

[0127] Emergency resources primarily include mobile power sources (MPSs), such as electric vehicles (EVs), mobile energy storage systems (MESSs), and mobile emergency generators (MEGs), which have the potential to significantly enhance the resilience of power distribution systems to extreme weather events. By pre-positioning emergency resources in the power distribution system before extreme events occur, rapid pre-recovery can be achieved, thereby enhancing the sustainability of power supply to critical loads.

[0128] The objective function is as follows:

[0129]

[0130] in, This is a 0-1 variable; if the emergency resource m is pre-set to node i, the value is 1, otherwise it is 0. The variable is 0-1, where a value of 1 indicates a closed branch and a value of 0 indicates an open branch. For the virtual power of branch ij, g i To provide power to the virtual power source of node i, u ij p is a variable ranging from 0 to 1, where a value of 1 indicates that the branch is intact and a value of 0 indicates that the branch is faulty. i / q i P represents the actual active and reactive loads of node i. i / Q i For the injected active and reactive power at node i, pf ij / qf ij The active and reactive power of branch ij, Let be the load demand priority weight for node i, and N be the set of nodes.

[0131] The outermost optimization coordinates the pre-provisioning of emergency resources with the distribution network topology. Distribution Network Reconfiguration (DNR) is also employed to coordinate the pre-provisioning of emergency resources to better utilize their potential to enhance distribution network survivability. To ensure that each emergency resource (such as a mobile power supply MPS) can only be pre-provisioned to one of the candidate nodes and the number of emergency resources connected to each site, the following constraints are added:

[0132]

[0133]

[0134] Where, N mLet M be the set of nodes that the m-th mobile emergency resource can connect to, and M be the set of all emergency resources. i Cap is the set of emergency resources that can be connected to node i. i The number of mobile emergency resource connections allowed for node i.

[0135] To ensure that the distribution network maintains a radial topology, the following constraints must also be met:

[0136]

[0137]

[0138]

[0139] in, The line on / off status variable (binary variable, 0 or 1) indicates whether branch (i,j) is selected for operation. i Let L be the virtual load of node i, L be the set of branches, and N and N' be the virtual load of node i. sub For the total number of nodes and substations, I ij N represents the virtual load of node i. sub For the substation collection, K l It is a sufficiently large positive number.

[0140] To avoid the isolation of individual substation nodes, the following constraints were also added:

[0141]

[0142]

[0143] In the middle layer of the above objective function, for the emergency resource provisioning and distribution network reconfiguration decisions derived from the outermost layer, we seek a set of damaged branches that minimizes the weighted sum of survivability loads. Branch damage is constrained by the following constraints:

[0144]

[0145]

[0146] in, For the set of vulnerable branches, and These represent the maximum number of faults in the vulnerable branch and the non-vulnerable branch, respectively. This constraint is the boundary between the number of faults in the vulnerable branch and the non-vulnerable branch mentioned above.

[0147] The innermost problem, based on the decisions derived from the outermost layer and the system damage situation in the intermediate layer, also needs to satisfy relevant constraints such as active power balance constraints, reactive power balance constraints, active power injection constraints and reactive power constraints of candidate nodes, power constraints of nodes not connected to MPS, power constraints of affected nodes, power factor constraints, line apparent power constraints, branch power flow, and voltage amplitude. The weighted sum of surviving loads is maximized through the following constraints:

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] Where S and V are the sets MESS and EV, These are the upper limits of active and reactive power in MPS, respectively. and These represent the active and reactive power demands of the load at node i, respectively. For the maximum capacity of the branch, Let K2 be the square of the voltage at node i, and K2 be a sufficiently large positive number. ij x ij For branch resistance and reactance, , v represents the minimum and maximum values ​​of node i, respectively. i The square of the voltage magnitude at node i (i.e. The above constitutes the power generation balance constraint for emergency resources.

[0160] In summary, the objective function and its constraints have been clarified.

[0161] It should be noted that the objective function described above is a two-stage robust optimization problem, solved using the C&CG algorithm. For simplicity, the model is rewritten in a compact form:

[0162]

[0163] Where y, u, and z are decision variables for emergency resource pre-positioning and network reconfiguration, uncertain variables for branch damage, and decision variables for power allocation, respectively, and their feasible regions are Y, U, and Z, respectively.

[0164] By performing a dual transformation on the innermost max problem and combining it with the intermediate min problem, the min-max subproblem in the above equation becomes:

[0165]

[0166]

[0167]

[0168]

[0169] in, For the given values ​​of variable y, These are the dual variables. The main problem takes the following form:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] Where K is the number of iterations.

[0176] It should be noted that the subproblems and main problem mentioned above, as well as the constraints added in the update iteration, do not have actual physical meaning. This is because the core of solving the problem is the above-mentioned max-min-max objective function and all the above-mentioned constraints. Although these have actual physical meaning, they cannot be solved simply. Therefore, by using the mathematical method of dual transformation, the entire objective function and constraints are equivalently transformed, which is equivalent to changing the original problem, which had practical meaning but was difficult to solve, into another form that has no practical meaning but can be solved.

[0177] In summary, the main problem, subproblems, objective function, and constraints have been clearly defined. Furthermore, the optimal tolerance mentioned above is a pre-defined allowable error threshold; the smaller the optimal tolerance, the more accurate the solution. The aforementioned candidate resource allocation scheme is an arbitrarily selected feasible initial solution in the early stages of iterative computation. During the candidate iterative computation, this scheme is gradually replaced by better schemes until it finally converges to the optimal solution. The main problem is part of the computational process in solving the problem. The input of the main problem is the currently known set of failure scenarios, and the output is the optimal resource allocation scheme under the constraints of these failure scenarios, along with its theoretical score. The subproblems are another part of the solution process. The input of a subproblem is a specific allocation scheme produced by the main problem, and the output is the worst possible combination of failure scenarios under that scheme, along with its lower bound score for that scenario. The lower bound score is the value produced by the subproblem, representing the actual power supply that the current allocation scheme can achieve under the worst-case scenario, thus reflecting the scheme's ability to withstand harsh environments. The upper bound score is the value produced by the main problem, representing the highest power supply that the current allocation scheme can theoretically achieve under the constraints of the currently considered fault scenarios. As new fault scenario constraints are added during the iteration process, the upper bound score will gradually decrease and approach the actual feasible value.

[0178] In this embodiment, a feasible resource allocation scheme that satisfies all the above physical constraints is first selected as the initial solution y. * This refers to the aforementioned candidate resource allocation scheme. Then, using this candidate scheme as known conditions, a subproblem is calculated. Under the constraints of this scheme, the subproblem iterates through all possible fault combinations to determine the fault scenario that minimizes the objective function, thus obtaining the... This refers to the fault combination that causes the greatest disruption to the current power supply scheme. The objective function value obj output by the subproblem is denoted as the lower bound score of the candidate scheme. At this point, we have LB = obj, UB = +∞, K = 1, u1 = .

[0179] S520 introduces the fault scenario that minimizes the objective function value as a new constraint into the main problem. Solving the main problem yields an optimized allocation scheme. The objective function value corresponding to the optimized allocation scheme is calculated to obtain the upper bound score of the optimized allocation scheme. The main problem is used to formulate emergency allocation schemes, and the sub-problems are used to evaluate fault scenarios.

[0180] In this embodiment of the application, the fault scenario that minimizes the objective function as determined above is added as a new constraint to the calculation of the main problem. This means that when formulating the allocation plan, the main problem must consider the possibility of the fault scenario that minimizes the objective function occurring and formulate corresponding strategies.

[0181] Subsequently, under the combined constraints of the fault scenario that minimizes the objective function and the basic constraints mentioned above, the main problem is re-optimized to obtain the optimized allocation scheme, which is: The pre-positioned locations of emergency resources, the on / off states of switches in the distribution network, and the emergency dispatch strategy are redefined. Simultaneously, the optimal objective function value achievable under the current known fault scenarios is output, denoted as the upper bound score. At this point, UB is updated to UB = At this point, check the difference between UB and LB.

[0182] S530 determines the emergency resource allocation plan based on the lower bound score, the upper bound score, and the optimal tolerance.

[0183] After completing one round of alternating solutions to the main problem and subproblems, the difference between the obtained upper and lower bound scores is compared. If the difference is less than or equal to the optimal tolerance ε, i.e., UB-LB≤ε, it means that the score of the currently obtained optimized allocation plan is close enough to the actual disaster resistance baseline, and the optimized allocation plan is directly implemented. The output is the actual emergency resource allocation plan that is executed.

[0184] If the difference between the two is greater than or equal to the optimal tolerance, it means that the power supply limit promised by the current scheme cannot be achieved under severe scenarios, and further iteration is needed. This involves inputting the current optimized allocation scheme into a subproblem, identifying the fault scenario that minimizes the objective function under this optimized allocation scheme, and updating the lower bound score. Then, this new fault scenario is added as a new constraint to the main problem, and the main problem is solved again to update the upper bound score. This process is repeated until the difference between the upper and lower bound scores converges to the optimal tolerance range.

[0185] In this embodiment, by iterating alternately between the main problem and subproblems and gradually approximating the upper and lower bounds of the scores, a better emergency resource allocation scheme can be obtained within a finite number of iterations, rather than relying on experience to obtain an approximate feasible solution. Compared with deterministic scenario optimization methods, this method explicitly considers the set of uncertainties in disaster scenarios, and the generated scheme has theoretical guarantees even under severe conditions.

[0186] In an exemplary embodiment, an emergency resource allocation plan is determined based on a lower bound score, an upper bound score, and an optimal tolerance, including:

[0187] The calculation ends when the difference between the upper and lower bound scores is less than or equal to the optimal tolerance, and an emergency resource allocation plan is obtained based on the optimized allocation plan.

[0188] In one exemplary embodiment, such as Figure 6 As shown, based on the lower bound score, upper bound score, and optimal tolerance, the emergency resource allocation plan is determined, including:

[0189] S610: When the difference between the upper bound score and the lower bound score is detected to be greater than the optimal tolerance, the sub-problem is solved according to the optimized allocation scheme to obtain the intermediate fault scenario that minimizes the objective function value, and the updated lower bound score is obtained according to the objective function and the current lower bound score.

[0190] Among them, the intermediate fault scenario refers to the subproblem finding a fault combination that minimizes the objective function value for the current optimization and allocation scheme during the iterative solution process.

[0191] In this embodiment, when the difference between the upper and lower bound scores exceeds the optimal tolerance, iterative calculation of the allocation scheme is required. The optimized allocation scheme is input into the sub-problem module. Under the premise of this scheme, the sub-problem searches for the fault scenario that minimizes the objective function value, i.e., the intermediate fault scenario. The optimal value of the objective function, obj, is calculated under the intermediate fault scenario. This optimal value is then compared with the lower bound score of the current record. The larger of the two is taken as the updated lower bound score, i.e., LB = max(LB, obj).

[0192] S620: Add intermediate failure scenarios as new constraints to the main problem, solve the main problem to obtain intermediate resource allocation schemes, calculate the corresponding objective function value based on the intermediate resource allocation schemes, and obtain the updated upper bound score.

[0193] The intermediate resource allocation scheme refers to the updated scheme obtained by resolving the main problem after adding new constraints, which includes the pre-set nodes of mobile emergency resources and the on / off status of each switch in the distribution network.

[0194] In this embodiment, the intermediate fault scenarios discovered by the sub-problem are added as new constraints. Based on all the original fault scenario constraints, the main problem, along with the added constraints, is re-optimized to solve for the resource allocation scheme, resulting in a new resource allocation scheme, namely the aforementioned intermediate resource allocation scheme. Simultaneously, the objective function value achievable by this scheme under all known fault scenario constraints is output, and this value serves as the updated upper bound score.

[0195] S630, return to the step of determining the emergency resource allocation plan based on the lower bound score, the upper bound score, and the optimal tolerance.

[0196] The algorithm re-compares the updated upper and lower bound scores to see if the difference has narrowed to the optimal tolerance range. If the difference is less than the optimal tolerance, the iteration stops, and the current resource allocation scheme is output as the final scheme. If the difference is still greater than or equal to the optimal tolerance, S610 and S620 need to be repeated until the difference between the upper and lower bound scores is less than the optimal tolerance, at which point the iteration ends. That is, if UB - LB ≤ optimal tolerance ε, then return y. *And end the calculation; otherwise, update K=K+1 and set u. K =u * Generate variable z according to the following formula K Constraints:

[0197]

[0198]

[0199]

[0200]

[0201] This application also provides a preferred embodiment of a method for emergency resource allocation in power distribution networks in response to rainstorm disasters.

[0202] Based on the formula for heavy rainfall intensity in Guangdong and the Chicago rainfall pattern with a single peak, and combined with the actual geographical features and land cover data of a certain area, the constructed heavy rainfall model is analyzed, and the effectiveness of the proposed method is verified in the modified IEEE 33-node test system.

[0203] First, this example uses a 30m resolution DEM (Digital Elevation Model), setting the drainage capacity of the area to 1.52. The ANUGA 2D hydrodynamic simulation library is used for simulation, with a simulation time interval of 1 minute. The study investigates the urban waterlogging depth under a 10-year return period rainstorm, with a rainfall duration of 120 minutes and a rainfall peak location coefficient of 0.438. The resulting rainfall process diagram is shown below. Figure 7 As shown in the figure, the rainfall return period P=10 refers to a simulated 10-year return period rainfall scenario. The urban flooding scenario is shown in the figure below. Figure 8 As shown, different colors represent the distribution of flooding in urban areas. From Figure 8 As can be seen, water began to accumulate on the ground at 55 minutes, and within minutes the water accumulation rapidly reached its most severe level, with large-scale water accumulation occurring in most areas of the ground.

[0204] Then, emergency resources are pre-configured, including the maximum number of faults in vulnerable and non-vulnerable branches. The values ​​were set to 5 and 3 respectively, and the results were compared and analyzed through post-disaster emergency resource simulation scheduling.

[0205] like Figure 9 As shown, assuming there are 3 electric vehicle charging stations and 3 mobile energy storage stations in the test system, the distribution of vulnerable branches in the system is obtained based on the failure probability obtained from the rainstorm disaster simulation. The red lines represent vulnerable branches in the system, and the black parts represent non-vulnerable branches. Figure 9Solid lines represent closed branch / section switches during normal operation, allowing electrical energy to pass through and forming a radial power supply network for the system. Dashed lines represent open tie branches / tie switches during normal operation, initially not energized. This is the default initial state of the IEEE 33-node system. During distribution network reconfiguration or fault recovery, some dashed tie switches can be closed while some solid branch switches are opened to change the power supply path, keeping the network radial and preventing the formation of closed loops.

[0206] The Gurobi model is used for solving. Figure 10 The convergence curve of the model is shown, and the solution results are shown in Table 1. Before the rainstorm disaster, the disconnected lines in the network reconstruction were 9-10, 20-21, 21-22, 6-26, and 29-30, respectively. Electric vehicle access node 5, mobile energy storage device access node 15, and mobile generator access node 29.

[0207] Table 1 Results of Emergency Resource Pre-positioning and Distribution Network Reconfiguration

[0208]

[0209] Assuming that electric vehicles and mobile energy storage devices are fully charged before the rainstorm, and mobile generators are refueled before the rainstorm, and that pre-positioned emergency resources are quickly connected to the power supply system shortly after the event, a dynamic scheduling method is used to analyze the pre-positioning results of emergency resources, such as... Figure 11 As shown in the figure. The results show that, with the emergency resource pre-positioning strategy proposed in this invention, the system has a significant improvement in post-disaster load recovery, verifying the effectiveness of the proposed method.

[0210] In summary, the emergency resource pre-positioning method for distribution networks under urban rainstorm disasters proposed in this invention can effectively improve the distribution network's ability to prevent extreme rainstorm disasters before disasters, and enhance the system's resilience and survivability after disasters occur.

[0211] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0212] Based on the same inventive concept, this application also provides an emergency resource allocation device for distribution networks in response to rainstorm disasters, which implements the aforementioned method for emergency resource allocation in distribution networks in response to rainstorm disasters. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the emergency resource allocation device for distribution networks provided below can be found in the limitations of the emergency resource allocation method for distribution networks described above, and will not be repeated here.

[0213] In one exemplary embodiment, such as Figure 12 As shown, a power distribution network emergency resource allocation device is provided, comprising:

[0214] Module 1201 is used to acquire rainfall evolution data for the target area;

[0215] The water accumulation calculation module 1202 is used to determine the water accumulation change data of each catchment area in the target area based on the rainstorm evolution data;

[0216] The determination module 1203 is used to determine vulnerable and non-vulnerable branches in the distribution network based on the water accumulation change data of each catchment area;

[0217] The generation module 1204 is used to solve a preset objective function with the fault number boundary in vulnerable and non-vulnerable branches and the preset balance of emergency resource power generation as constraints, to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of emergency resources and emergency strategies; the objective function is used to maximize the power supply of the distribution network.

[0218] Each module in the aforementioned power distribution network emergency resource allocation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0219] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the distribution network emergency resource allocation method as described in any of the above embodiments.

[0220] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the distribution network emergency resource allocation method as described in any of the preceding embodiments.

[0221] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the distribution network emergency resource allocation method as described in any of the preceding embodiments.

[0222] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0223] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0224] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for emergency resource allocation in power distribution networks in response to rainstorm disasters, characterized in that, The method includes: Acquire data on the evolution of heavy rainfall in the target area; Based on the rainstorm evolution data, determine the water accumulation change data of each catchment area in the target region; Based on the water accumulation change data of each catchment area, vulnerable and non-vulnerable branches in the distribution network are identified; Using the fault number boundaries and emergency resource access constraints in the vulnerable and non-vulnerable branches as constraints, a preset objective function is solved to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset locations of the emergency resources and emergency strategies. The objective function is used to maximize the power supply of the distribution network.

2. The method according to claim 1, characterized in that, The acquisition of rainfall evolution data for the target area includes: Obtain the formula for expressing the intensity of a rainstorm; the formula for expressing the intensity of a rainstorm includes the correspondence between the rainstorm return period, the duration of rainfall, and the rainfall parameters; Based on the obtained rain peak location coefficient and the rainstorm intensity expression formula, the rainstorm evolution data is calculated, which includes pre-peak data and post-peak data.

3. The method according to claim 1 or 2, characterized in that, The step of determining the changes in water accumulation in each catchment area of ​​the target region based on the rainstorm evolution data includes: Multiple catchment areas are obtained by dividing the target area based on the differences in elevation information; For each catchment area, the change in water accumulation in the catchment area is determined based on the rainfall in the catchment area, the drainage in the catchment area, and the flow between the catchment area and adjacent catchment areas.

4. The method according to claim 1 or 2, characterized in that, The determination of vulnerable and non-vulnerable branches in the distribution network based on the changes in water accumulation in each catchment area includes: Determine the geographical location of each branch in the power distribution network; For each branch road, the geographical location is mapped to each of the water catchment areas to determine the water catchment area to which the branch road belongs; If the water accumulation in the catchment area to which the branch belongs is detected to be greater than or equal to a preset water accumulation threshold, the branch is determined to be the vulnerable branch. If the water volume in the catchment area to which the branch belongs is less than the water volume threshold, the branch is determined to be a non-vulnerable branch.

5. The method according to claim 1 or 2, characterized in that, The method involves solving a preset objective function, using the fault number boundary in the vulnerable and non-vulnerable branches and a preset balance of emergency resource power generation as constraints, to obtain an emergency resource allocation scheme, including: Obtain the preset optimal tolerance, select any feasible candidate resource allocation scheme as the initial solution, and solve the sub-problem based on the fault number boundary in the vulnerable branch and the non-vulnerable branch, the preset emergency resource power generation balance as constraints, and the candidate resource allocation scheme to obtain the fault scenario that minimizes the objective function value, and use the corresponding objective function value as the lower bound score of the candidate resource allocation scheme. The fault scenario that minimizes the objective function is added as a new constraint to the main problem. The main problem is solved to obtain an optimized allocation scheme. The objective function value corresponding to the optimized allocation scheme is calculated to obtain the upper bound score of the optimized allocation scheme. The main problem is used to formulate an emergency allocation scheme, and the sub-problems are used to evaluate the fault scenarios. The emergency resource allocation plan is determined based on the lower bound score, the upper bound score, and the optimal tolerance.

6. The method according to claim 5, characterized in that, The step of determining the emergency resource allocation plan based on the lower bound score, the upper bound score, and the optimal tolerance includes: The calculation ends when the difference between the upper bound score and the lower bound score is less than or equal to the optimal tolerance, and the emergency resource allocation scheme is obtained according to the optimized allocation scheme.

7. The method according to claim 5, characterized in that, The step of determining the emergency resource allocation plan based on the lower bound score, the upper bound score, and the optimal tolerance includes: If the difference between the upper bound score and the lower bound score is detected to be greater than the optimal tolerance, the sub-problem is solved according to the optimized allocation scheme to obtain the intermediate fault scenario that minimizes the objective function, and the updated lower bound score is obtained according to the objective function and the current lower bound score. The intermediate failure scenario is added as a new constraint to the main problem. The intermediate resource allocation scheme is obtained by solving the main problem. The corresponding objective function value is calculated based on the intermediate resource allocation scheme to obtain the updated upper bound score. Return to the step of determining the emergency resource allocation plan based on the lower bound score, the upper bound score, and the optimal tolerance.

8. A power distribution network emergency resource allocation device for rainstorm disasters, characterized in that, The device includes: The acquisition module is used to acquire data on the evolution of heavy rainfall in the target area; The water accumulation calculation module is used to determine the water accumulation change data of each catchment area in the target area based on the rainstorm evolution data; The determination module is used to determine vulnerable and non-vulnerable branches in the distribution network based on the water accumulation change data of each catchment area; The generation module is used to solve a preset objective function with the fault number boundary and emergency resource access constraint in the vulnerable branch and the non-vulnerable branch as constraints to obtain an emergency resource allocation scheme. The emergency resource allocation scheme includes the preset location of the emergency resources and the emergency strategy. The objective function is used to maximize the power supply of the distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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