Elastic power distribution network planning method considering wind and rain microtopography coupling and digital system

Abstract

The invention discloses an elastic power distribution network planning method and digital system considering wind and rain microtopography coupling, and relates to the technical field of power system disaster prevention and planning, and the method comprises the steps: constructing a typhoon wind field model considering microtopography correction, recognizing ridge features based on a ridge recognition algorithm, and outputting a wind speed acceleration ratio; simulating spatial and temporal distribution of rainfall in combination with a corrected wind field and a terrain uplift effect, constructing a two-dimensional hydrodynamic rainfall flood waterlogging model, and outputting a ponding evolution process of each grid region; a combined failure model and a node failure probability function are constructed for an overhead line, an underground cable and power distribution node equipment, a typical disaster scene is generated and reduced after fusion, and a two-stage mixed integer stochastic programming model is constructed; and solving the two-stage mixed integer stochastic programming model by adopting an improved dual decomposition algorithm, and solving dual gap global optimum by combining branch and bound processing. According to the method, the expected power failure loss under the extreme event is reduced, and the toughness support under the extreme environment is improved.

Description

Technical Field

[0001] This invention relates to the field of power system disaster prevention and planning technology, specifically to a flexible distribution network planning method and digital system that takes into account the coupling of wind, rain and micro-topography. Background Technology

[0002] Frequent extreme weather events such as typhoons pose a significant threat to the safety of urban energy infrastructure, especially the distribution network, which serves as the end point of power supply. Due to its high physical exposure and complex topology, it is highly susceptible to large-scale failures under the combined impact of strong winds and torrential rains. With the deepening of resilient city construction, accurately assessing the vulnerability of the distribution network under extreme disasters and formulating scientific pre-disaster prevention plans and emergency dispatch strategies accordingly has become a critical issue that urgently needs to be addressed in the power system field.

[0003] However, existing methods for assessing the resilience of power distribution networks still have significant limitations. On the one hand, most studies focus primarily on the impact of single disaster factors, such as considering only the damage of strong winds to overhead lines, while neglecting the fact that typhoons are often accompanied by extreme heavy rainfall. Urban flooding caused by torrential rain can submerge critical power nodes such as underground cables and ring main units; this cascading failure effect caused by wind and rain is often simplified or ignored in existing assessment models. On the other hand, existing wind field models are mostly based on macroscopic scales and lack detailed consideration of micro-topography (such as ridges and slopes). In real urban environments, micro-topography has a significant accelerating effect on local wind speeds; without micro-topography correction, the assessment of the risk of damage to power equipment in specific areas can be severely biased.

[0004] Furthermore, existing resilience enhancement research typically separates pre-disaster preventative configuration from in-disaster recovery operations, lacking a comprehensive, collaborative optimization framework. Existing emergency resource allocation models often fail to effectively address the high uncertainties of extreme disaster scenarios and suffer from low solution efficiency during large-scale computations. Therefore, developing a distribution network resilience assessment and configuration method that considers the coupling effects of wind, rain, and micro-topography, and integrates full-chain fault prediction with two-stage stochastic programming, is of significant practical importance for enhancing the ability of modern urban power grids to withstand extreme disasters. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing power distribution network resilience assessment methods have problems such as single assessment factors, macroscopic wind field models not considering the effect of micro-topography on local wind speed acceleration, lack of wind-rain physical coupling mechanism leading to inaccurate disaster assessment, and how to separate pre-disaster preventive configuration from disaster recovery operations.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a flexible power distribution network planning method considering the coupling of wind, rain, and micro-topography, comprising constructing a typhoon wind field model considering micro-topography correction, identifying ridge features based on a ridge identification algorithm and outputting the wind speed acceleration ratio; simulating the spatiotemporal distribution of rainfall by combining the corrected wind field and the topographic lifting effect, and constructing a two-dimensional hydrodynamic stormwater flooding model to output the water accumulation evolution process of each grid area; constructing a combined failure model and a node failure probability function for overhead lines, underground cables, and distribution node equipment, fusing them to generate and reduce typical disaster scenarios, and constructing a two-stage mixed integer stochastic programming model; using an improved dual decomposition algorithm to solve the two-stage mixed integer stochastic programming model, and combining branch and bound processing to find the global optimum of the dual gap.

[0008] As a preferred embodiment of the flexible power distribution network planning method considering wind and rain micro-topography coupling described in this invention, the construction of the typhoon wind field model considering micro-topography correction includes constructing a basic wind field model based on historical typhoon data, wherein wind field pressure and atmospheric wind speed are expressed as: , , in, For wind field pressure, The pressure at the center of the cyclone. Due to environmental pressures, The radius of maximum wind speed. The distance from the center of the cyclone. To determine the parameters of cyclone intensity and shape, Atmospheric wind speed, air density, For Coriolis force parameters.

[0009] As a preferred embodiment of the flexible power distribution network planning method considering wind, rain and micro-topography coupling described in this invention, the ridge identification algorithm includes: determining the optimal profile analysis window using the mean change point analysis method; finding the change point where the curve changes from steep to gentle by using the natural logarithm of the average terrain undulation under different grid sizes; determining the optimal window size of the statistical unit; and based on the determined window size, performing terrain gradient longitudinal profile evaluation to identify local elevation maxima as ridge feature points.

[0010] As a preferred embodiment of the flexible distribution network planning method considering wind, rain, and micro-topography coupling described in this invention, the simulated spatiotemporal distribution of rainfall includes: using a TCR model to combine the corrected surface wind field with topographically induced upward airflow to simulate the spatiotemporal distribution of rainfall intensity, where rainfall intensity is expressed as: , , in, Rainfall intensity, For rainfall efficiency, It is water vapor. It is liquid water. saturated specific humidity, Vertical wind speed, For horizontal wind speed, This refers to the slope of the terrain.

[0011] As a preferred embodiment of the flexible power distribution network planning method considering wind and rain micro-topography coupling described in this invention, the construction of the two-dimensional hydrodynamic stormwater flooding model includes iterative output based on the momentum conservation equation, wherein the water flow exchange velocity between grids is expressed as: , in, It is the acceleration due to gravity. For the water depth that changes over time, This refers to the water surface elevation. Unit conversion factor, This is the Manning coefficient. For water exchange velocity, For penetration rate, This refers to the intensity of rainfall.

[0012] As a preferred embodiment of the flexible power distribution network planning method considering wind, rain and micro-topography coupling described in this invention, the construction of the combined failure model includes, for overhead lines, combining the corrected real-time wind speed and direction data, outputting the wind load on the conductors and poles, constructing a combined failure model that includes the probability of conductor breakage and pole collapse, the overhead line failure model considers the combined influence of wind speed and wind direction, and the failure rate of the entire overhead line is output through the series system model.

[0013] As a preferred embodiment of the flexible power distribution network planning method that takes into account the coupling of wind, rain and micro-topography described in this invention, the node failure probability function includes: establishing a node failure probability function based on the water level rise curve, according to the node inundation depth output by the two-dimensional hydrodynamic stormwater flooding model and combined with the equipment flood control height threshold.

[0014] As a preferred embodiment of the flexible distribution network planning method considering wind, rain, and micro-topography coupling described in this invention, the construction of the two-stage mixed integer stochastic programming model includes: the first stage is pre-disaster preventive configuration, which aims to minimize the expected investment cost and power outage loss under all preset scenarios, and optimizes the placement location and capacity of distributed emergency power sources; the second stage is in-disaster emergency dispatch, which dynamically reconstructs the distribution network based on the state of the interconnection switch in response to the real-time evolution of specific disaster scenarios, and coordinates the dispatch of emergency power sources and microgrid output.

[0015] As a preferred embodiment of the resilient distribution network planning method considering wind, rain, and micro-topography coupling described in this invention, the step of using an improved dual decomposition algorithm to solve the two-stage mixed integer stochastic programming model includes: decoupling complex unpredictable constraints through Lagrange dual relaxation, decomposing them into independent scenario subproblems for parallel output, embedding a branch and bound framework to handle integer variable constraints, and performing a global optimal solution for the resilience configuration of large-scale distribution networks by iteratively optimizing the dual gap.

[0016] Another objective of this invention is to provide a resilient digital distribution network system that takes into account the coupling of wind, rain and micro-topography. This system can simulate the spatiotemporal distribution of rainfall by combining the modified wind field and the topographic lifting effect, and construct a two-dimensional hydrodynamic stormwater flooding model to output the water accumulation evolution process of each grid area. This solves the problem that current distribution network resilience assessment technologies have a single assessment factor.

[0017] As a preferred embodiment of the resilient digital distribution network system considering wind-rain micro-topography coupling described in this invention, it includes: a micro-topography-corrected wind field modeling module, a wind-rain-waterlogging physical coupling module, an equipment failure probability generation module, a pre-disaster prevention configuration module, and an emergency dispatch and reconfiguration module during a disaster; the micro-topography-corrected wind field modeling module is used to determine ridge feature points and geometric parameters through a ridge identification algorithm, calculate the average wind speed acceleration ratio using the ASCE standard, and correct the atmospheric wind speed generated by the Holland wind field model to obtain the actual near-surface wind speed distribution affected by local micro-topography; the wind-rain-waterlogging physical coupling module is used to simulate rainfall distribution through a TCR model, construct a stormwater and waterlogging model using elevation data, and iteratively calculate... The system calculates the network water accumulation process and ultimately quantifies the flooding depth of distribution network equipment. The equipment failure probability generation module calculates the wind loss probability of overhead lines and the rain loss probability of underground equipment, and after fusing the two, generates a spatiotemporally correlated failure scenario set through Monte Carlo simulation and extracts typical disaster scenarios. The pre-disaster prevention configuration module constructs the first stage of a two-stage mixed integer stochastic programming model, which is pre-disaster preventive configuration. It optimizes the placement and capacity of distributed emergency power sources to minimize the expected total cost. The in-disaster emergency dispatch and reconstruction module constructs the second stage of a two-stage mixed integer stochastic programming model, which is in-disaster emergency dispatch. It dynamically reconstructs the distribution network and systematically dispatches emergency resources to maximize the protection of power supply to important loads.

[0018] The beneficial effects of this invention are: This invention provides a flexible power distribution network planning method that considers the coupling of wind, rain, and micro-topography. It constructs a typhoon wind field model that incorporates micro-topography correction, identifies ridge features based on a ridge identification algorithm, outputs the wind speed acceleration ratio, determines ridge feature points and geometric parameters within the feature area, obtains the actual near-surface wind speed distribution affected by local micro-topography, and simulates the spatiotemporal distribution of rainfall by combining the corrected wind field and the topographic lifting effect. Furthermore, it constructs a two-dimensional hydrodynamic model of rainwater runoff and urban flooding, outputting the water accumulation evolution process in each grid area. This effectively identifies system vulnerabilities hidden beyond single-factor models, making fault prediction results closer to actual disaster scenarios. For overhead lines, underground cables, and distribution node equipment, it constructs a combined failure model and a node failure probability function. After fusion, typical disaster scenarios are generated and reduced, and a two-stage mixed-integer stochastic programming model is constructed to extract typical disaster scenarios, providing practical and computationally feasible inputs for subsequent optimization. While accurately identifying system vulnerabilities, it achieves both investment economy and power supply reliability. An improved dual decomposition algorithm is used to solve the two-stage mixed-integer stochastic programming model, and branch and bound processing is combined to find the global optimum of the dual gap, significantly improving the solution efficiency and quality of the two-stage mixed-integer stochastic programming model and reducing the computational complexity of large-scale stochastic programming. This invention achieves better results in the fine-grained reconstruction of disaster scenarios, the economic and reliability optimization of defense strategies, and the efficient solution of large-scale computations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The above is an overall flowchart of a flexible power distribution network planning method that takes into account the coupling of wind, rain and micro-topography, as provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a schematic diagram of the ridge identification algorithm logic and micro-topography correction principle of a flexible power distribution network planning method that takes into account the coupling of wind, rain and micro-topography, as provided in Embodiment 1 of the present invention.

[0022] Figure 3 This is a schematic diagram of the water exchange principle of a two-dimensional fluid dynamics stormwater flooding model for an elastic power distribution network planning method that takes into account wind, rain and micro-topography coupling, as provided in Embodiment 1 of the present invention.

[0023] Figure 4 The logical relationship and flowchart of a two-stage mixed integer stochastic programming model for a flexible distribution network planning method that takes into account wind, rain and micro-topography coupling provided in Embodiment 1 of the present invention.

[0024] Figure 5 This is a geographical mapping diagram of an improved IEEE 33-node system for a flexible distribution network planning method that takes into account wind, rain and micro-topography coupling, as provided in Embodiment 2 of the present invention.

[0025] Figure 6 The graph shows the water accumulation depth curves of each node in an urban power distribution network under a single scenario, which is a flexible power distribution network planning method that takes into account the coupling of wind, rain and micro-topography, as provided in Embodiment 2 of the present invention.

[0026] Figure 7 The convergence curve of the solution process based on the dual decomposition algorithm is shown in Embodiment 2 of the present invention for a flexible distribution network planning method that takes into account the coupling of wind, rain and micro-topography.

[0027] Figure 8 In a specific fault scenario (such as Scenario 8) of the flexible distribution network planning method that takes into account wind, rain and micro-topography coupling provided in Embodiment 2 of the present invention, the distribution network forms an islanded operation and emergency power output status diagram through reconfiguration. Detailed Implementation

[0028] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0029] Example 1, referring to Figures 1-4 As an embodiment of the present invention, a flexible distribution network planning method considering wind, rain, and micro-topography coupling is provided, comprising: S1: Construct a typhoon wind field model that takes into account micro-topography correction, identify ridge features based on ridge recognition algorithm and output wind speed acceleration ratio.

[0030] Specifically, a ridge identification algorithm based on mean change point analysis and topographic gradient profile evaluation is proposed to determine the ridge feature points and their geometric parameters in a specific area. By combining the ASCE standard to calculate the average wind speed acceleration ratio, the atmospheric wind speed generated by the Holland wind field model is corrected, thereby obtaining the actual near-surface wind speed distribution affected by local micro-topography.

[0031] Constructing typhoon wind field models that incorporate micro-topography corrections includes, for example: Figure 2 The basic wind field model is constructed based on historical typhoon data, and the wind field pressure and atmospheric wind speed are expressed as follows: , , in, For wind field pressure, The pressure at the center of the cyclone. Due to environmental pressures, The radius of maximum wind speed. The distance from the center of the cyclone. To determine the parameters of cyclone intensity and shape, Atmospheric wind speed, air density, For Coriolis force parameters.

[0032] It should be noted that the ridge identification algorithm includes: using the mean change point analysis method to determine the optimal profile analysis window; using the natural logarithm of the average terrain undulation under different grid sizes to find the change point where the curve changes from steep to gentle; and determining the optimal window size of the statistical unit; based on the determined window size, performing terrain gradient longitudinal profile evaluation and identifying local elevation maxima as ridge feature points.

[0033] It should also be noted that the spatial distribution of the near-surface actual wind speed field, generated by the Holland wind field model and corrected for terrain, uses the geographical region as the base and overlays different colored thermal layers to represent the surface wind speed after correction according to the ASEC standard. The method for correcting atmospheric wind speed using the ASCE standard includes calculating the average wind speed acceleration ratio, expressed as: , , , , in, The average wind speed acceleration ratio, Parameters that reflect terrain features and maximum acceleration effect, To adjust the parameters of the acceleration effect as a function of horizontal distance, To adjust the parameters of acceleration effect as a function of altitude, The slope angle of the terrain. To calculate the horizontal distance from the coordinate point to the ridge point, To correct for the coefficient of horizontal influence, it controls the rate at which the terrain acceleration effect decays in the horizontal direction, depending on the terrain slope. At that time, the value of this parameter is 1.5. The horizontal distance from the foot of the windward side of the mountain to the midpoint of the ridge height. To determine the rate at which the terrain acceleration effect decays with increasing altitude, This is used to calculate the vertical height of the point above the ground.

[0034] The corrected actual surface wind speed is expressed as follows: , in, This refers to the actual surface wind speed. This refers to atmospheric wind speed.

[0035] It should also be noted that by combining mean change point analysis with the ASCE standard, an innovative ridge identification and wind speed acceleration ratio calculation method is proposed to achieve accurate quantification of local micro-topographic effects in typhoon wind fields. This effectively solves the key problem of traditional typhoon models in accurately depicting near-surface wind speed distribution under complex terrain, and provides key wind speed input for power equipment combined failure models.

[0036] S2: Combines modified wind field and topographic lifting effect to simulate the spatiotemporal distribution of rainfall, and constructs a two-dimensional fluid dynamics model of rainwater flooding, outputting the water accumulation evolution process of each grid area.

[0037] Specifically, the simulated spatiotemporal distribution of rainfall includes: using the TCR model, combining the corrected surface wind field with topographically induced upward airflow to simulate the spatiotemporal distribution of rainfall intensity, which is expressed as: , , in, Rainfall intensity, For rainfall efficiency, It is water vapor. It is liquid water. saturated specific humidity, Vertical wind speed, For horizontal wind speed, This refers to the slope of the terrain.

[0038] Vertical wind speed The vertical velocity is estimated by summing the values ​​generated by five physical mechanisms: topographic forcing, friction effect, vortex extension, baroclinic effect, and radiative cooling. The main term is derived from the topographic slope. With horizontal wind speed Calculate the product.

[0039] It should be noted that a two-dimensional fluid dynamics model for stormwater flooding is constructed based on elevation data, such as... Figure 3 As shown, the water accumulation evolution process of each grid area is calculated iteratively based on the momentum conservation equation, and the submersion depth of distribution network node equipment (such as ring main units and key parts of substations) is quantified.

[0040] Constructing a two-dimensional hydrodynamic model of stormwater flooding includes iteratively outputting the momentum conservation equation, with the water exchange velocity between grids expressed as: , in, It is the acceleration due to gravity. For the water depth that changes over time, This refers to the water surface elevation. Unit conversion factor, This is the Manning coefficient. For water exchange velocity, For penetration rate, This refers to the intensity of rainfall.

[0041] Each computational grid in The depth of the water at any moment Determined through iteration, and expressed as: , in, For grid in The depth of the water at any moment The neighborhood flow in the four basic directions of the grid. The amount of wastewater generated through the city's drainage network. For grid length, For penetration rate, Rainfall intensity, For grid in The depth of the water at any given moment.

[0042] The drainage volume is calculated using the number of drainage wells, the drainage coefficient, and the cross-sectional area of ​​the drainage wells, and is expressed as follows: , in, The number of drainage wells. This is the drainage coefficient. The cross-sectional area of ​​the drainage well. For drainage wells in The depth of the water at any given moment.

[0043] It should also be noted that by deeply coupling the modified wind field, the topographic lifting effect, and the two-dimensional fluid dynamics model, a refined spatiotemporal dynamic simulation of urban flooding caused by extreme rainfall can be achieved. This accurately quantifies the evolution of the inundation depth of key power distribution equipment nodes, solves the problem that traditional methods cannot accurately assess the dynamic impact of floods on underground cables and node equipment, and provides high-precision risk input data for power grid disaster prevention.

[0044] S3: For overhead lines, underground cables and power distribution node equipment, construct a combined failure model and node failure probability function, fuse them to generate and reduce typical disaster scenarios, and construct a two-stage mixed integer stochastic programming model 100.

[0045] Specifically, the power equipment failure probability model based on wind-rain physical coupling includes the overhead line failure model and the distribution node failure model.

[0046] The construction of the combined failure model includes, for overhead lines, combining the corrected real-time wind speed and direction data, outputting the wind load on the conductors and poles, constructing a combined failure model that includes the probability of conductor breakage and pole collapse. The failure model of the overhead line takes into account the combined influence of wind speed and direction, and the failure rate of the entire overhead line is output through the series system model.

[0047] The failure model for overhead lines takes into account the combined effects of wind speed and direction, and its failure rate per unit length of line is... Pole failure rate They are represented as follows: , , in, The probability of failure under normal conditions. The wind direction influence coefficient. This is the critical wind speed. For absolute destructive wind speed, This is the sensitivity coefficient to wind speed.

[0048] The failure rate of the entire overhead line is calculated using a series system model and is expressed as follows: , in, For the failure rate of overhead lines, The first in this overhead line The failure probability of utility poles. The first in this overhead line The probability of failure of a section of the line.

[0049] It should be noted that the node failure probability function includes establishing a node failure probability function based on the water level rise curve, which is based on the node inundation depth output by the two-dimensional hydrodynamic stormwater flooding model and combined with the equipment flood control height threshold.

[0050] The failure model for power distribution nodes is determined based on the relationship between the node inundation depth, the design flood control height, and the cable joint elevation, and is expressed as follows: , in, This represents the probability of node failure. These are the fitted parameters in the exponential function. For the node flooding depth, To design flood control height, This refers to the elevation of the cable joint.

[0051] When the water depth meets At that time, the probability of node failure Using an exponential function fit, the overall reliability of the segmented branch is determined jointly by the probability of wind loss along the line and the probability of flooding at both ends, expressed as: , in, To ensure the overall reliability of segmented branches, For the failure rate of overhead lines, For nodes The probability of failure of power distribution equipment. For nodes Probability of power distribution equipment failure.

[0052] It should also be noted that the construction of the two-stage mixed integer stochastic programming model 100 includes the following: the first stage 101 is pre-disaster preventive configuration, which aims to minimize the expected investment cost and power outage loss under all preset scenarios and optimize the placement location and capacity of distributed emergency power sources; the second stage 102 is in-disaster emergency dispatch, which dynamically reconstructs the distribution network through the status of the interconnection switch in response to the real-time evolution of specific disaster scenarios and coordinates the dispatch of emergency power sources and microgrid output.

[0053] The overall logic of the two-stage mixed integer stochastic programming model 100 is as follows: Figure 4 As shown, the complete process from typical disaster scenario input to the final resilient defense solution is presented. The typical failure scenario set and its probability distribution are generated based on the wind-rain physical coupling model as input, including the first stage 101 pre-disaster configuration and the second stage 102 in-disaster optimization.

[0054] The objective function of the first stage of the two-stage mixed integer stochastic programming model 100 is to minimize the expected power outage cost across all scenarios, expressed as: , in, For mathematical expectation, Here, "random variable" refers to an uncertain disaster scenario. For the first stage of decision-making, it represents the location scheme of the emergency power supply. In order to make decisions And the random scene is In the case of the second stage, the minimum loss is the power outage loss. For the scene The probability of occurrence In specific scenarios Below, based on the site selection scheme The calculated minimum power outage loss cost.

[0055] The first phase of constraints 101 includes total investment limits and binary variable constraints on the location of emergency power supplies, expressed as: , , in, Fixed investment costs associated with a node include land acquisition fees, design fees, installation service fees, etc., required for installing equipment at the node. These costs may vary for different nodes. Cost per unit power of emergency power supply equipment This refers to the rated power of the emergency power supply. For binary decision variables, Indicates at node Install emergency power supply. This is the upper limit of the total investment budget. The candidate node set is the set of all nodes in the power grid that are allowed to install emergency power supplies.

[0056] The second stage 102 of the two-stage mixed-integer stochastic programming model 100, targeting a specific fault scenario, minimizes load reduction losses through distribution network reconfiguration and emergency power supply dispatch. Its objective function is expressed as: , in, The unit load shedding cost reflects the importance of the load and is used to distinguish load priorities. The load shedding rate is the decision variable for the second stage. The active power requirement indicates the scenario. Next, node The amount of electricity load originally required.

[0057] The second phase of 200 constraints includes power flow constraints based on the linear DistFlow model, node power balance constraints, voltage deviation range constraints, and radial constraints to maintain the radial topology of the network. The detailed constraints are as follows: , , , , , , , , , , , , , , in, For nodes voltage amplitude, For nodes voltage amplitude, For the line The resistance, For the line Reactance, The active power flow on the line, The reactive power flow on the line, As the reference voltage, This is a line connection status variable (0-1 variable), where 1 represents a closed switch. It is a sufficiently large constant (Big-M). This refers to the active power injected into the node from the main grid (upper-level grid) (typically only non-zero at substation nodes). This refers to the reactive power injected into the node from the main grid (upper-level grid) (typically only non-zero at substation nodes). The active power output of the emergency power supply. The reactive power output of the emergency power supply. To retain the actual active and reactive load of the service, Only when an emergency power supply is installed, Power is limited to the rated power. For nodes The lower limit of active power injected from the main network. For nodes The upper limit of active power injected from the main network. For nodes The lower limit of reactive power injected from the main grid. For nodes The upper limit of reactive power injected from the main grid. The rated power of the emergency power supply equipment. For the line The maximum active power flowing through the system. For the line The maximum reactive power flowing through the system. For load shedding rate, This represents the lower limit of the node voltage. This represents the upper limit of the node voltage. These are the line integrity parameters, known parameters generated by the wind and rain fault model. A value of 1 indicates the line is intact in the scenario, while a value of 0 indicates the line is damaged in the scenario. To ensure the switch functions only when the wiring is intact, For the first in the network A basic loop, in which at least one line must be broken (i.e., all...). The sum cannot equal the number of loop edges, thus disrupting the loop structure.

[0058] It should also be noted that by constructing a combined failure model of overhead lines and underground cables that integrates the physical coupling of wind, rain, and flood, the failure probability of power equipment under extreme disasters can be accurately quantified. Based on this, a set of typical disaster scenarios is generated, and a two-stage stochastic programming model for pre-disaster preventive configuration and in-disaster emergency dispatch is established. This provides a scientific decision-making framework that takes into account both economy and reliability for improving the resilience of the distribution network, and solves the problems of accurate identification and resource optimization of weak links in the power grid under extreme weather conditions.

[0059] S4: An improved dual decomposition algorithm is used to solve the two-stage mixed integer stochastic programming model 100, and the dual gap global optimum is obtained by combining branch and bound processing.

[0060] Specifically, the improved dual decomposition algorithm is used to solve the two-stage mixed integer stochastic programming model 100. This includes decoupling complex unpredictable constraints through Lagrange dual relaxation, decomposing them into independent scenario subproblems for parallel output, embedding a branch and bound framework to handle integer variable constraints, and iteratively optimizing the dual gap to find the global optimal solution for the resilience configuration of large-scale distribution networks.

[0061] Relaxing unpredictable constraints across scenarios using the Lagrange duality relaxation method. Constructing the Lagrange dual function The original problem is decomposed into multiple independent subproblems and solved in parallel. The Lagrangian function of each subproblem is expressed as: , in, For the scene The Lagrange function value is the value assigned to the scenario after the algorithm decomposes the overall problem. The objective function is optimized separately. It includes the original cost plus a "penalty term" for violating constraints. For the scene The first-stage copy of the decision variables in the original problem, (EPS addressing) is globally unique. However, in the dual decomposition algorithm, in order to enable parallel computation of different scenarios, we first assume that each scenario has its own addressing scheme. Then, they are forced to be equal through constraints (i.e. This is achieved through non-predictive constraints. Achieved For the scene The second-stage decision variable vector contains all the decision variables in this scenario. For the scene The probability of occurrence Cost coefficient vector (including load shedding penalty unit price) wait), For equivalent to , The unexpected constraint matrix is ​​a coefficient matrix consisting of 1s, 0s, and -1s, which is mathematically used to represent "all scenarios". It is a matrix form that "must be equal". Then, it is appended to the objective function through Lagrange relaxation, thus forming... item.

[0062] It should be noted that during the branch and bound process, the subgradient method is used to solve the dual problem to obtain the global lower bound, and a feasible solution is found through a heuristic method to update the global upper bound until the gap between the upper and lower bounds converges to the preset accuracy.

[0063] Heuristic methods decompose the original large problem into multiple independent subproblems in the dual decomposition algorithm. Each scenario calculates what it considers the optimal EPS location scheme. However, since the disaster situation is different in each scenario, the calculated location schemes are often different, which violates the "unexpectedness constraint". Therefore, when the solutions of different scenarios are inconsistent, a heuristic function is called. Usually, the logic of "voting" or "rounding" is used to integrate the inconsistent opinions of different scenarios into a single, unified candidate scheme, quickly find a potentially feasible global solution, and use it to update the upper bound of the problem.

[0064] A feasible solution is one that satisfies all the constraints in the problem. In this invention, a solution called a "feasible solution" must pass two hurdles simultaneously: the first-stage constraint and the second-stage constraint. The first-stage constraint requires that the generated unified site selection scheme must meet physical and budgetary constraints, must be a binary variable (only 0 or 1), the total investment cost cannot exceed the budget limit, and can only be installed from the allowed set of candidate nodes. The second-stage constraint requires that this fixed site selection scheme can be applied to every disaster scenario and a corresponding operating scheme can be found. It must meet all physical quantitative, equipment, and network reconfiguration topology constraints. In any scenario, even if a disconnection occurs, the load shedding amount must be between 0 and 1. There cannot be a situation where there is no solution. A solution that meets the above conditions is a feasible solution.

[0065] The preset precision is a stopping standard set by the user, referring to the relative difference between the currently found global upper bound (UB) and global lower bound (LB). The upper bound is the cost of the best feasible solution found so far, and the lower bound is the theoretical minimum cost calculated through mathematical relaxation.

[0066] It should also be noted that by improving the dual decomposition algorithm and combining it with branch and bound, and relaxing cross-scenario unexpected constraints, the original problem is decomposed into parallel solvable independent scenario subproblems. This effectively handles integer variable constraints and duality gaps, achieving an efficient global optimal solution to the large-scale distribution network resilience configuration problem, and improving computational efficiency and solution accuracy.

[0067] Example 2, refer to Figures 5-8 As an embodiment of the present invention, a flexible distribution network planning method considering the coupling of wind, rain and micro-topography is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0068] First, the software environment is MATLAB R2024b, with mathematical modeling using the YALMIP toolbox and the commercial solver Gurobi used for solving the underlying optimization problem. The hardware computing platform uses an AMD Ryzen 9 7950X CPU with 16 cores and 32 threads, a base frequency of 4.50GHz, and 64GB of system memory.

[0069] An improved IEEE 33-bus distribution system was used as a test case, and it was mapped to a... In the geographic coordinate system. The topographic data for this area was acquired using ASTER GDEM with a spatial resolution of 30 meters. The topology of the example system is as follows. Figure 5 As shown, it includes 33 busbar nodes and 37 branch lines, among which the lines are classified as underground cables based on their physical properties (e.g., Figure 5 (as shown by the red line in the middle) and overhead lines (such as...) Figure 5 (As shown by the black line). The critical wind speed for the overhead power line segment is set. The absolute destructive wind speed is 20 m / s. The critical wind speed for the pole section is set at 45 m / s. The absolute destructive wind speed is 30 m / s. The speed is 60 m / s. The probability of normal line failure. Set the value to 0.0002 for utility poles and 0.0001 for power poles, and set the vegetation coefficient according to the vegetation cover. Between 1 and 1.5.

[0070] Regarding load configuration and economic parameters, loads are categorized into three levels based on their importance. Class I loads are located at bus nodes 3, 4, 5, 11, 14, 25, and 31, with a corresponding outage compensation cost of 21.6 yuan / kWh. Class II loads are located at bus nodes 2, 6, 7, 10, 15, 23, 29, 32, and 33, with a compensation cost of 7.2 yuan / kWh. Class III loads have a compensation cost of 0.8 yuan / kWh. For resilience enhancement measures, the maximum power of a single EPS (Emergency Power Supply) device is set. For 500kW, the unit cost The price is 3000 yuan per unit of power, and the total system investment is capped at [amount missing]. Set to 10,000,000 yuan.

[0071] In the process of micro-topography wind field modeling, the system first determines the optimal profile analysis window based on the mean change point analysis method. The grid, through terrain gradient longitudinal profile evaluation, identifies local ridge feature points. Combining correction factors calculated according to the ASCE standard, the atmospheric wind speed generated by the Holland wind field model is locally accelerated and corrected to obtain an actual wind speed field that reflects near-surface microclimate characteristics. Regarding the rainfall and flood model parameters, the rainfall efficiency is set to 0.9, the saturated specific humidity to 0.0125 g / g, and the infiltration efficiency to 0.3. The cable joint height above ground is uniformly set at power distribution nodes. The design height was set to 300mm, and different flood control design heights were set for different nodes. These calculation environments and parameter settings constitute the operational basis of the toughness optimization configuration method of this invention.

[0072] After completing the basic parameter modeling, the system enters the full-chain disaster scenario generation stage. First, based on the modified Holland wind field model, the system simulates the dynamic evolution of the typhoon over time. As the typhoon center moves, the wind speed within each geographic grid changes continuously over time. At this point, the system calls upon ridge identification results to compensate for wind speed in grids located in terrain acceleration zones. For example, when the average atmospheric wind speed is 35 m / s, the real-time wind speed at the ridgeline after ASCE correction can reach over 42 m / s, significantly improving the calculated physical load value for overhead lines (such as a 10kV line section) in that area.

[0073] Secondly, the system uses the TCR (Tropical Cyclone Rainfall) physical model to synchronously generate rainfall scenes and calculates the vertical wind speed caused by terrain forcing. By combining atmospheric saturated specific humidity data, a high spatial resolution map of instantaneous rainfall distribution is generated. Subsequently, this rainfall data serves as input to a two-dimensional fluid dynamics model, simulating the runoff and accumulation of rainwater on urban surfaces. The water surface elevation of each grid is calculated iteratively using the momentum conservation equation, enabling real-time monitoring of the flooding status of key nodes in the power distribution network. For example... Figure 6 As shown, at the 240th minute of the simulation, the water level curve of bus node No. 24, which is located in the catchment area, showed that the water level had risen rapidly from the initial 50mm to 337mm, exceeding the flood control threshold of the node. The system then determined that the node and its downstream branch had entered the "flood failure" state.

[0074] Finally, wind loss and rain loss are probabilistically fused. For each distribution branch, the system uses a series system failure model to comprehensively calculate the wind loss probability of all poles, the conductor breakage probability, and the flooding probability of nodes on both sides of the line. In this process, a spatiotemporal correlation calculation method is adopted, meaning that the failure probability depends not only on the current instantaneous wind speed and water level, but also on the cumulative effect of disasters (such as fatigue damage to the tower structure caused by continuous strong winds). Through the above physical coupling logic, the system uses Monte Carlo sampling to generate thousands of initial random scenarios within each simulation step, and further uses Simultaneous Backward Reduction to reduce the large set of scenarios into 13 statistically representative typical failure scenarios. Each scenario contains the line state vector (0 or 1) and its occurrence probability for each time period, providing input for subsequent optimization decisions.

[0075] After extracting typical disaster scenarios and their probability distributions, a two-stage mixed-integer stochastic programming model was used to conduct resilient defense decision-making. The first stage of decision-making mainly addresses the pre-disaster preventative resource allocation problem. Based on the preset total investment budget, the system selects the optimal emergency power supply (EPS) installation scheme from 16 candidate bus nodes (1, 4, 5, 6, 7, 11, and 14). The algorithm determines the installation of 500kW rated power emergency power supply equipment at bus nodes 3, 4, 5, 11, 14, 25, and 31 by solving the objective function, which minimizes the sum of investment cost and expected power outage loss across all scenarios. The selection of these locations fully considers the vulnerability of the area to wind-rain combined failure and provides a physical basis for islanded operation during disasters by reserving dispatchable energy near critical load centers in advance.

[0076] In the second phase, the decision-making logic shifts to emergency dispatch optimization tailored to specific real-time evolution scenarios. When a typhoon enters the urban microclimate area and induces line faults, the system optimizes switch states and load protection strategies in real time based on the EPS placement scheme determined in the first phase. It calculates branch power flow using a linear DistFlow model and dynamically adjusts tie switches in the 33-node system. In the event of a large-scale power outage, the system reconstructs the original radial topology into multiple independent microgrid islands by disconnecting severely faulty branches and closing tie lines.

[0077] Within these reconfigured power grid islands, the system coordinates the output of emergency power supplies and the remaining available loads. Based on load priority classification, the system prioritizes ensuring continuous power supply to Category I loads (such as critical loads at buses 3, 4, 5, and 11), followed by Category II loads, and selectively reduces Category III loads when energy is constrained. For example... Figure 7 As shown, in scenario S8, although more than 50% of the system's lines fail due to wind damage or flooding, the EPS equipment can still ensure power output through the two-stage collaborative decision-making described in this invention, ensuring that 86.5% of the power demand of a certain type of load is met. This deeply coupled logic, from pre-disaster static configuration to in-disaster dynamic reconfiguration, maximizes the resilience and support value of flexible resources, reducing the overall power outage loss cost by 87.47% compared to traditional defense solutions that lack reconfiguration capabilities.

[0078] In terms of algorithm execution efficiency and convergence analysis, the dual decomposition algorithm with an embedded branch and bound framework exhibits excellent performance in handling complex two-stage stochastic programming problems. On a computing platform configured with a 16-core CPU and 16GB of memory, the algorithm's total running time for 13 typical typhoon disaster scenarios is only 170.2 seconds. Figure 8 As shown, the optimal solution gap (GAP) converged to 1.49% by the 46th iteration, and fully converged by the 339th iteration, at which point only 4.92% of the total state space had been explored. To verify the robustness of the algorithm, 100 independent and repeated experiments were conducted. The results showed that the average number of iterations was stable at 394.88, the average running time was stable at 196.69 seconds, and the 95% confidence interval fluctuated very little. This proves that the algorithm has extremely high reliability and computational efficiency in dealing with large-scale distribution network integer programming problems, and can meet the decision-making needs for rapid pre-disaster response in power engineering.

[0079] The solution described in this invention has extremely high application and promotion value in actual power systems. Furthermore, the model's prediction accuracy was verified in real time using micro-meteorological monitoring devices deployed at substation sites. Field monitoring data showed that the disaster intensity output by the model of this invention was slightly higher than the actual observed value. This moderate conservative bias provides necessary safety margins for power grid operators and aligns with the defensive logic of urban infrastructure resilience planning. The final experimental results show that by implementing the pre-configuration and dynamic reconfiguration collaborative strategy recommended in this invention, power grid companies can achieve full recovery of investment costs under conditions where typhoon impact duration exceeds 72.04 hours, while ensuring an 87.47% reduction in power outage losses. In summary, the method constructed in this invention not only achieves deep coupling of multiple factors such as wind, rain, and micro-topography technically, but also possesses significant commercial application prospects in terms of economic feasibility.

[0080] Example 3, an embodiment of the present invention, provides a resilient digital distribution network system that takes into account the coupling of wind, rain and micro-topography, including a micro-topography correction wind field modeling module, a wind-rain-waterlogging physical coupling module, an equipment failure probability generation module, a pre-disaster prevention configuration module, and an emergency dispatch and reconstruction module during a disaster.

[0081] Among them, the micro-topography correction wind field modeling module is used to determine the ridge feature points and geometric parameters through the ridge identification algorithm, and calculate the average wind speed acceleration ratio in combination with the ASCE standard to correct the atmospheric wind speed generated by the Holland wind field model, so as to obtain the actual near-surface wind speed distribution affected by local micro-topography.

[0082] The wind-rain-waterlogging physical coupling module is used to simulate rainfall distribution through the TCR model, combine it with elevation data to construct a stormwater and waterlogging model, iteratively calculate the network water accumulation process, and finally quantify the inundation depth of distribution network equipment.

[0083] The equipment failure probability generation module is used to calculate the wind loss probability of overhead lines and the rain loss probability of underground equipment respectively. After fusing the two, a set of spatiotemporally correlated failure scenarios is generated through Monte Carlo simulation, and typical disaster scenarios are extracted.

[0084] The pre-disaster prevention configuration module is used to construct the first stage 101 of the two-stage mixed integer stochastic programming model 100, which is a pre-disaster prevention configuration that minimizes the expected total cost by optimizing the placement and capacity of distributed emergency power sources.

[0085] The disaster emergency dispatch and reconfiguration module is used to construct the second stage 102 of the two-stage mixed integer stochastic programming model 100. Disaster emergency dispatch is carried out by dynamically reconfiguring the distribution network and systematically dispatching emergency resources to maximize the power supply of important loads.

[0086] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the flexible power distribution network planning method that takes into account wind, rain and micro-topography coupling as proposed in the above embodiment.

[0087] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the flexible power distribution network planning method that takes into account wind, rain and micro-topography coupling as proposed in the above embodiment.

[0088] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0090] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0091] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A flexible power distribution network planning method considering the coupling of wind, rain, and micro-topography, characterized in that, include: A typhoon wind field model that takes into account micro-topography correction is constructed, and ridge features are identified based on the ridge identification algorithm and the wind speed acceleration ratio is output. The spatiotemporal distribution of rainfall is simulated by combining the modified wind field and the orographic lifting effect, and a two-dimensional fluid dynamics model of rainwater flooding is constructed to output the water accumulation evolution process of each grid area; For overhead lines, underground cables, and power distribution node equipment, a combined failure model and a node failure probability function are constructed. After fusion, typical disaster scenarios are generated and reduced, and a two-stage mixed integer stochastic programming model is constructed. An improved dual decomposition algorithm is used to solve a two-stage mixed integer stochastic programming model, and the dual gap is found by combining branch and bound.

2. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claim 1, characterized in that: The typhoon wind field model that incorporates micro-topography correction includes... A basic wind field model is constructed based on historical typhoon data, and the wind field pressure and atmospheric wind speed are expressed as follows: ， ， in, For wind field pressure, The pressure at the center of the cyclone. Due to environmental pressures, The radius of maximum wind speed. The distance from the center of the cyclone. To determine the parameters of cyclone intensity and shape, Atmospheric wind speed, air density, For Coriolis force parameters.

3. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claim 1 or 2, characterized in that: The ridge identification algorithm includes, The optimal profile analysis window is determined by using the mean change point analysis method. By using the natural logarithm of the average terrain undulation under different grid sizes, the change point where the curve changes from steep to gentle is found, and the optimal window size of the statistical cell is determined. Based on a defined window size, a longitudinal profile evaluation of the terrain gradient is performed, and local elevation maxima are identified as ridge feature points.

4. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claim 3, characterized in that: The spatiotemporal distribution of the simulated rainfall includes: Using the TCR model, the modified surface wind field is combined with topographically induced upward airflow to simulate the spatiotemporal distribution of rainfall intensity, which is expressed as: ， ， in, Rainfall intensity, For rainfall efficiency, It is water vapor. It is liquid water. saturated specific humidity, Vertical wind speed, For horizontal wind speed, This refers to the slope of the terrain.

5. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claims 1, 2, or 4, characterized in that: The construction of the two-dimensional fluid dynamics stormwater flooding model includes, Based on the iterative output of the momentum conservation equation, the water flow exchange velocity between grids is expressed as: ， in, It is the acceleration due to gravity. For the water depth that changes over time, This refers to the water surface elevation. Unit conversion factor, This is the Manning coefficient. For water exchange velocity, For penetration rate, This refers to the intensity of rainfall.

6. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claim 5, characterized in that: The construction of the combined failure model includes, For overhead lines, the wind load on conductors and poles is output by combining the corrected real-time wind speed and direction data. A combined failure model is constructed that includes the probability of conductor breakage and pole collapse. The failure model of overhead lines takes into account the combined effects of wind speed and direction. The failure rate of the entire overhead line is output through the series system model.

7. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claims 1, 2, 4, or 6, characterized in that: The node failure probability function includes, Based on the node inundation depth output by the two-dimensional hydrodynamic stormwater flooding model, and combined with the equipment flood control height threshold, a node failure probability function based on the water level rise curve is established.

8. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claim 7, characterized in that: The construction of the two-stage mixed integer stochastic programming model includes, The first phase is pre-disaster preventative configuration, which aims to minimize the expected investment costs and power outage losses under all preset scenarios, and optimize the placement and capacity of distributed emergency power supplies. The second stage is emergency dispatch during disasters. In response to the real-time evolution of specific disaster scenarios, the power distribution network is dynamically reconfigured through the status of interconnection switches, and emergency power sources and microgrid outputs are coordinated and dispatched.

9. The flexible distribution network planning method considering wind, rain, and micro-topography coupling as described in claims 1, 2, 4, 6, or 8, characterized in that: The method of solving the two-stage mixed integer stochastic programming model using the improved dual decomposition algorithm includes... Complex unpredictable constraints are decoupled by Lagrange dual relaxation, decomposed into independent scenario subproblems for parallel output, and a branch-and-bound framework is embedded to handle integer variable constraints. Global optimal solutions for the resilience configuration of large-scale distribution networks are obtained by iteratively optimizing the dual gap.

10. A flexible distribution network digital system considering the coupling of wind, rain, and micro-topography, employing the flexible distribution network planning method considering the coupling of wind, rain, and micro-topography as described in any one of claims 1 to 9, characterized in that: It includes a micro-topography correction wind field modeling module, a wind-rain-waterlogging physical coupling module, an equipment failure probability generation module, a pre-disaster prevention configuration module, and an emergency dispatch and reconstruction module during a disaster. The micro-topography correction wind field modeling module is used to determine the ridge feature points and geometric parameters through the ridge identification algorithm, calculate the average wind speed acceleration ratio in combination with the ASCE standard, correct the atmospheric wind speed generated by the Holland wind field model, and obtain the actual near-surface wind speed distribution affected by local micro-topography. The wind-rain-waterlogging physical coupling module is used to simulate rainfall distribution through the TCR model, combine elevation data to construct a stormwater and waterlogging model, iteratively calculate the network water accumulation process, and finally quantify the inundation depth of distribution network equipment. The equipment failure probability generation module is used to calculate the wind loss probability of overhead lines and the rain loss probability of underground equipment respectively. After fusing the two, a spatiotemporally correlated failure scenario set is generated through Monte Carlo simulation, and typical disaster scenarios are extracted. The pre-disaster prevention configuration module is used to construct the first stage of a two-stage mixed integer stochastic programming model, which is a pre-disaster prevention configuration. It optimizes the placement and capacity of distributed emergency power supplies to minimize the expected total cost. The disaster emergency dispatch and reconfiguration module is used to construct the second stage of a two-stage mixed integer stochastic programming model. Disaster emergency dispatch is achieved by dynamically reconfiguring the distribution network and systematically dispatching emergency resources to maximize the power supply to critical loads.

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