Elastic evaluation method and system for power distribution system to cope with urban rainstorm and waterlogging disasters

Abstract

The invention discloses an elastic evaluation method and system for a power distribution system to cope with urban rainstorm waterlogging disasters, and the method comprises the steps: constructing a rainstorm model according to the rainfall capacity and the rainfall process, and obtaining the time sequence rainfall intensity; a runoff production model and a confluence model are constructed, the surface water depth change process is determined according to the obtained time sequence rainfall intensity, and a surface water depth simulation result is obtained; building a model based on a surface water depth simulation result to simulate outdoor equipment submerging and indoor equipment submerging processes, and obtaining a power distribution system disaster scene and a damaged equipment set; constructing a road network model based on a power distribution system disaster scene and a damaged equipment set, and modeling a mobile resource path problem under urban inland inundation as a time-dependent vehicle routing problem; and constructing a mixed integer linear programming model as a power distribution system response model based on a time-dependent vehicle routing problem, quantifying the elasticity of the power distribution system by using an elasticity measurement index, and realizing elasticity evaluation of urban rainstorm waterlogging disasters based on the obtained elasticity index Rt.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster prevention and mitigation of power distribution systems, and specifically relates to a method and system for evaluating the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters. Background Art

[0002] Frequent urban flooding disasters caused by heavy rains pose a huge risk to the safe and stable operation of distribution systems. The wide spatial distribution and low-standard design of distribution system facilities increase the vulnerability of distribution systems and make them prone to failure. The resilience of distribution systems reflects their ability to resist, absorb, adapt and recover from disasters in a timely and efficient manner. Quantifying resilience is the basis for formulating effective disaster prevention and mitigation strategies, and existing technologies mainly focus on assessing the potential scale of power outages under flood disasters.

[0003] In order to evaluate the impact of rain and flood disasters on the power system, the existing technology combines the hydrological model with the power system model. For the distribution system, the distribution nodes such as substations, switch cabinets, ring network cabinets, and distribution transformers are mainly regarded as vulnerable parts, and the evaluation indicators are mainly concentrated on the probability of equipment failure, the number of power outage users, the amount of load loss, and economic losses. Existing research mainly focuses on estimating the potential scale of power outages under the threat of floods. However, the severity of power outages includes not only its scale, but also the speed of recovery. It is necessary to consider the recovery aspect in the evaluation. Due to insufficient consideration of the post-disaster recovery process, existing research has failed to fully evaluate the recovery capacity of the distribution system under flood disaster conditions.

[0004] In the modeling of post-disaster recovery of distribution systems, current research mainly combines the power restoration problem with the vehicle routing problem. At present, there are several mature modeling paradigms that apply various types of mobile emergency resources (MER) to the post-disaster recovery of distribution systems, including edge-based models, vertex-based models, traffic flow allocation models, and spatiotemporal network models. However, there are two significant characteristics of rainstorm waterlogging disasters that are different from other disasters: the downtime of distribution equipment is the sum of the flooding time and repair time of the component location, and the component repair can only start when the rain subsides; the routing and scheduling of MER depends largely on the accessibility of the site. Since road flooding is one of the most typical consequences of urban waterlogging, rainstorms will hinder the repair force from reaching the location of the distribution facilities, further delaying equipment maintenance and power restoration. In addition, the post-disaster travel time varies with the depth of road waterlogging, and the optimal path between the start-end pair may also change according to the waterlogging conditions of the road network. Therefore, the existing models cannot be directly applied to the resilience assessment of distribution systems under urban rainstorm waterlogging disasters. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for evaluating the resilience of a distribution system in response to urban rainstorm and waterlogging disasters in response to the above-mentioned deficiencies in the prior art, so as to solve the technical problems that the prior art does not give sufficient consideration to the post-disaster recovery process, and the existing models cannot be directly applied to the resilience evaluation of distribution systems in urban rainstorm and waterlogging disaster scenarios.

[0006] The present invention adopts the following technical solutions:

[0007] The resilience assessment method of the power distribution system to cope with urban rainstorm waterlogging disasters includes the following steps:

[0008] A rainstorm model was constructed based on rainfall amount and rainfall process to obtain the time series rainfall intensity;

[0009] Construct runoff generation model and runoff confluence model, determine the change process of surface water depth according to the obtained time series rainfall intensity, and obtain the surface water depth simulation result;

[0010] Based on the surface water depth simulation results, a model was built to simulate the flooding of outdoor equipment and indoor equipment, and the disaster scenario of the distribution system and the collection of damaged equipment were obtained;

[0011] A road network model is constructed based on the power distribution system disaster scenario and the damaged equipment set, and the mobile resource path problem under urban waterlogging is modeled as a time-dependent vehicle routing problem;

[0012] Based on the time-dependent vehicle routing problem, a mixed integer linear programming model is constructed as the distribution system response model. The elasticity metric index is used to quantify the elasticity of the distribution system. t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

[0013] Preferably, the rainstorm model is constructed according to the rainfall amount and rainfall process as follows:

[0014] An intensity-duration-frequency curve was constructed to obtain the average rainfall intensity. The Chicago rain type was used to design the rainfall process. The peak moment of the rainfall process was represented by the rain peak coefficient. The rainfall duration was divided into two periods before and after the peak. The instantaneous rainfall intensity before and after the peak was obtained based on the average rainfall intensity.

[0015] Preferably, the pre-peak and post-peak instantaneous intensities q1 and q2 of the rainstorm process are respectively:

[0016]

[0017] Among them, t1 and t2 represent the time before and after the peak of the rainstorm duration respectively; r represents the peak position coefficient of the design rainstorm process derived from historical rainstorm data; A, C, b, n r is the coefficient related to the city where the study area is located; P represents the return period.

[0018] Preferably, the flow generation model is constructed as follows:

[0019] f(t)=f c +(f0-f c ) -βt

[0020] Where f(t) is the infiltration rate at time t; f0 is the initial infiltration rate; f c is the stable infiltration rate of soil; β is the attenuation coefficient;

[0021] The confluence model is as follows:

[0022]

[0023] Among them, U represents the vector of conserved quantity; u x and u y represent the average velocity of water depth in the x-direction and the y-direction respectively; F x and F y They represent the flux in the x and y directions respectively; S represents the source term.

[0024] Preferably, the model is constructed to simulate the flooding of outdoor equipment and indoor equipment as follows:

[0025] The outdoor flooding model is as follows:

[0026] If the rain depth at outdoor node i exceeds the critical flood control standard threshold of the device Shut down the node urgently and disconnect nearby switches to isolate the fault. represents the set of outdoor flooded distribution nodes, h i (t) is the water level at outdoor node i at time t; represents a collection of distribution nodes; represents the set of indoor nodes. For each flooded outdoor node i, the rain depth at its location exceeds The time slot is recorded as Through the drainage simulation of the urban pipe network, the time slot of rainwater retreating from outdoor node i is recorded as

[0027] The indoor flooding model is as follows:

[0028] When the power distribution facilities are located indoors, the indoor flooding depth is calculated as a function of the outdoor water depth; let represents the set of distribution nodes with indoor water accumulation, where h in,i (t) is the indoor water depth of the building where node i is located at time t. The time slot corresponding to the occurrence of indoor waterlogging is also recorded as T i flood; The set of flooded distribution nodes is denoted as Introducing binary variable η i,t Indicates whether node i is normal at time t. For nodes where the rainwater has not risen above the critical water level, η i,t =1; the status of the flooded nodes during the rainstorm is as follows:

[0029]

[0030] in, Represents the indicator function.

[0031] Preferably, when the outdoor water depth exceeds the indoor water depth, the rainwater inflow into the building is calculated as follows:

[0032]

[0033] Where, Q is the flow rate of rainwater into the building; C d is the flow coefficient set to 1 in the present invention; L is the width of the building door expected to be flooded; h and h in Respectively represent the height of outdoor and indoor water surface;

[0034] Indoor water depths updated as follows:

[0035] h in (t+1)=h in (t)+Q(t)Δt / S in

[0036] Among them, S in is the building's floor area.

[0037] Preferably, the mobile resource routing problem under urban waterlogging is modeled as a time-dependent vehicle routing problem specifically as follows: a flooded road traffic model is established based on storm water depth and vehicle travel speed;

[0038] Define discrete arrival time functions to meet the needs of the spatiotemporal network modeling framework;

[0039] A space-time coordinate system is established with the time index as the horizontal coordinate and the road network vertex as the vertical coordinate to reflect the spatial changes of mobile resources in the road network at different times after the end of the rainstorm; based on the discrete arrival time function, the concept of space-time arc in the space-time coordinate system is defined, and the constraints related to post-disaster vehicle routing are expressed under the concept of space-time arc.

[0040] Preferably, the relationship between storm water depth and vehicle speed is:

[0041]

[0042] in, is the vehicle speed of the road section (u, v) at time t; is the normal vehicle speed of the road section (u,v); h uv (t) is the maximum water depth of the road section (u, v) at time t;

[0043] for Define the discrete arrival time function as a function And satisfy: a is non-decreasing; a(t)≥t(t=T r ,T min +1,…,T max );

[0044] Given a road network and a set of time slots Define the space-time arc in the space-time network as the arc from the vertex (u, t) to the vertex (v, τ) and satisfy: (u, v) ∈ ε or u = v; a uv (t) = τ; let this space-time arc be (uv, t)

[0045] The constraints are as follows:

[0046]

[0047]

[0048] Among them, x m,uv,t Is the mobile resource m on the space-time arc (uv, t), x m,uv,τ is whether the mobile resource m is on the space-time arc (uv,τ), is the set of space-time arcs in the space-time network, t / τ is the time slot index, is the time slot index set after the rainstorm, is the time slot corresponding to the end of the rainstorm, is the maximum simulation time of the model, A collection of mobile emergency resources. is the set of road network nodes, is the warehouse point of mobile resource m, Is the mobile resource m in the space-time arc superior.

[0049] Preferably, a mixed integer linear programming model is constructed as a distribution system response model, and elasticity metrics are used to quantify the elasticity of the distribution system to achieve elasticity assessment of urban rainstorm waterlogging disasters. Specifically:

[0050] The overall response model of the distribution system under waterlogging is modeled as a collaborative optimization model. The proportion of users with power outages of more than 12 hours to the total number of user interruptions is defined as the IEEE resilience index, including user interruptions automatically restored or avoided through intelligent switch operation and microgrid technology. Based on the solution results of the collaborative optimization model, the number of continuous user interruptions and the number of avoided user interruptions are counted to determine the parameterized resilience index R. t as follows:

[0051] Elasticity index R t as follows:

[0052]

[0053] Wherein, parameter t represents the duration of the power outage of interest; It is an indicator function to determine whether the duration of node power outage exceeds time t;

[0054] The collaborative optimization model is as follows:

[0055]

[0056] in, represents a set of time slot indices, represents the set of nodes in the power distribution system, δ i,t Indicates whether the load at node i is restored at time t, C i represents the number of users connected to node i, Δt represents the fixed time interval of each time slot, and ε represents a relatively small positive number. Represents a collection of mobile emergency resources, represents the set of space-time arcs in the space-time network, a uv (t) represents the arrival time of the route (u,v) at time t, x m,uv,t Indicates whether the mobile resource m is on the space-time arc (uv, t).

[0057] In a second aspect, an embodiment of the present invention provides a system for evaluating the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters, including:

[0058] The rainstorm module builds a rainstorm model based on rainfall and rainfall process to obtain the time series rainfall intensity;

[0059] The surface runoff module builds a runoff model and a confluence model, determines the change process of the surface water depth according to the obtained time-series rainfall intensity, and obtains the surface water depth simulation result; the equipment flooding module builds a model based on the surface water depth simulation result to simulate the outdoor equipment flooding and indoor equipment flooding process, and obtains the distribution system disaster scenario and the damaged equipment set;

[0060] The road flooding module builds a road network model based on the distribution system disaster scenario and the damaged equipment set, and models the mobile resource path problem under urban waterlogging as a time-dependent vehicle routing problem; the system response module builds a mixed integer linear programming model as a distribution system response model based on the time-dependent vehicle routing problem, and quantifies the elasticity of the distribution system using elasticity metrics. t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

[0061] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for assessing the resilience of a power distribution system to urban rainstorm and waterlogging disasters are implemented.

[0062] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating the resilience of a power distribution system to urban rainstorm and waterlogging disasters.

[0063] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for assessing the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters are implemented.

[0064] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for evaluating the resilience of a power distribution system to urban rainstorm and waterlogging disasters.

[0065] Compared with the prior art, the present invention has at least the following beneficial effects:

[0066] A method for evaluating the resilience of distribution systems to urban rainstorm and waterlogging disasters is proposed. According to the four elements of disaster risk analysis, a multidisciplinary assessment framework for urban distribution systems to cope with rainstorm and waterlogging disasters based on disaster scenario simulation is proposed. Five modules are used to capture the temporal evolution of flood disasters. Combined with the response behavior of the distribution system, the resilience of the system is more comprehensive and the evaluation results are more accurate.

[0067] Furthermore, heavy rain is the main cause of urban waterlogging, and constructing a heavy rain model is a necessary task for quantitatively studying the impact of heavy rain waterlogging disasters on the power distribution system. Designing a heavy rain model is to construct a heavy rain profile based on local historical heavy rain data. The heavy rain design process of the present invention mainly includes two steps: rainfall design and precipitation process design. The rainfall intensity is usually determined using the urban heavy rain intensity formula; considering that the unimodal rainfall process is more likely to cause serious flood disasters, the Chicago rain type is used for rainfall process design. By constructing a heavy rain model, the situation of urban waterlogging under different rainfall intensities can be simulated, and then the possible impact of heavy rain waterlogging on the power distribution system can be evaluated.

[0068] Furthermore, when the speed of rainstorm convergence exceeds the speed of rainwater loss, surface runoff will be generated on the urban ground. The surface runoff module consists of two parts: the runoff generation model and the confluence model. The runoff generation model is mainly used to calculate the amount of surface runoff generated during rainfall. Infiltration is the most critical factor in surface runoff generation. The Horton model is used to estimate the infiltration amount, and the infiltration process is regarded as a process with a continuously decreasing rate, which can accurately reflect the dynamic change process of soil infiltration capacity; the confluence model uses a two-dimensional shallow water equation to simulate how rainwater runoff moves on the surface after it is generated, and finally enters the drainage network through the stormwater outlet, fully considering the influence of terrain and boundary conditions on water flow, and improving the accuracy and reliability of the simulation results.

[0069] Furthermore, considering that the equipment related to the power distribution nodes may be located outdoors or indoors, models are established to simulate the flooding process of outdoor equipment and indoor equipment respectively, and to predict the flooding risks that the outdoor and indoor power distribution equipment may suffer.

[0070] Furthermore, in the context of flood disasters, the travel time of mobile resources between different locations is affected by road flooding conditions. Usually, after the rainstorm ends, the travel time decreases monotonically with the departure time. Therefore, the mobile resource path problem under urban waterlogging can be modeled as a time-dependent vehicle routing problem. By establishing a spatiotemporal network model based on arrival time function (TSN-ATF), the mobile resource path under urban waterlogging is modeled, so as to characterize the time-dependent characteristics of mobile resource travel after heavy rain, and dynamically determine the optimal travel route according to the time-varying rain depth on the road network.

[0071] Furthermore, considering various fault handling measures such as network reconstruction, drainage, equipment repair, and emergency power supply access, the distribution system response model is expressed as a mixed integer linear programming model. The optimal solution is quickly found while considering multiple factors, so that the number of households during power outages is minimized and the travel time of mobile emergency resources is minimized. Based on the IEEE resilience index, a parameterized IEEE resilience index is designed to more accurately quantify the recovery rate of the distribution system within the first 12 hours.

[0072] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0073] In summary, the present invention proposes a resilience assessment framework based on disaster scenario simulation, which focuses on the response behavior of the distribution system, including disaster resistance and post-disaster recovery. The resilience assessment of the distribution system under flood disasters is more comprehensive and the assessment results are more accurate. The characteristics of urban rainstorm waterlogging disasters are taken into account, and the established model takes into account the impact of equipment flooding time and road flooding on the response and resilience indicators of the distribution system, which supplements the application of post-disaster recovery modeling of the distribution system in urban rainstorm waterlogging scenarios. The parameterized IEEE resilience index is used to more accurately quantify the recovery efficiency of the distribution system within the first 12 hours.

[0074] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 Design for the flooding of distribution system equipment and power outage for users in a 50-year rainstorm;

[0076] Figure 2 To study the flooding of regional road networks under a design rainstorm with a return period of 50 years;

[0077] Figure 3 Gantt charts and system resilience curves for post-disaster mobile emergency resource scheduling;

[0078] Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0079] Figure 5 A block diagram of a chip provided according to an embodiment of the present invention;

[0080] Figure 6 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0083] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0084] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0085] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0086] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0087] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0088] The present invention provides a method for evaluating the resilience of a distribution system in response to urban rainstorm and waterlogging disasters, focusing on the response behavior of the distribution system, including disaster resistance and post-disaster recovery, so that the resilience evaluation of the distribution system under flood disasters is more comprehensive and the evaluation result is more accurate; considering the characteristics of urban rainstorm and waterlogging disasters, the established model takes into account the impact of equipment flooding time and road flooding on the response and resilience indicators of the distribution system, supplementing the application of post-disaster recovery modeling of the distribution system in urban rainstorm and waterlogging scenarios; and adopting parameterized IEEE resilience indicators to more accurately quantify the recovery efficiency of the distribution system within the first 12 hours.

[0089] See also Figure 6 The present invention provides a method for evaluating the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters, which specifically includes the following steps:

[0090] S1. Construct a rainstorm model based on rainfall and rainfall process;

[0091] The design rainstorm model is a model widely used in flood control projects that constructs a rainstorm process line based on local historical rainstorm data.

[0092] S101. Rainfall design: Rainfall intensity is usually determined using the urban rainstorm intensity formula, also known as the intensity-duration-frequency (IDF) curve, which describes the relationship between rainfall intensity, rainfall duration, and return period:

[0093]

[0094] Where, q is the average rainfall intensity (mm / min); t r represents rainfall duration (min); P represents return period (yr); A, C, b, n r is the coefficient associated with the city in which the study area is located.

[0095] S102. Rainfall process design: Rainfall process design is carried out based on the intensity-duration-frequency (IDF) curve. Considering that a single-peak rainfall process usually leads to serious floods and has an adverse impact on urban infrastructure, the Chicago rainfall pattern is used for rainfall process design. This model uses the rain peak coefficient r to indicate the peak moment of the rainfall process, thereby dividing the rainfall duration into two periods before and after the peak.

[0096] Based on formula (1), the instantaneous rainfall intensity before and after the peak can be deduced as:

[0097]

[0098]

[0099] Among them, q1 and q2 are the instantaneous intensities before and after the peak of the design rainstorm process (mm / min); t1 and t2 represent the corresponding time before and after the peak of the rainstorm duration (min); r∈(0,1) represents the peak position coefficient of the design rainstorm process derived from historical rainstorm data.

[0100] S2, construct runoff generation model and confluence model;

[0101] Design the surface runoff module: The runoff generation model and the confluence model together constitute the surface runoff module. When the rainstorm convergence speed exceeds the rainwater loss speed, surface runoff will be generated on the urban ground.

[0102] S201. Design of runoff model: Factors that affect rainwater loss include evaporation, depression water storage, interception, infiltration, etc. It is worth noting that in the simulation of urban waterlogging caused by heavy rain, infiltration is the most critical factor in surface runoff. Only the ground infiltration factor is considered here. The Horton model is used to estimate the infiltration volume. It believes that the infiltration process is a process with a continuously decreasing rate, and the rate of decrease is proportional to the duration of rainfall:

[0103] f(t)=f c +(f0-f c ) -βt (4)

[0104] Where, f(t) is the infiltration rate at time t (mm / min); f0 is the initial infiltration rate (mm / min); f c is the soil stable infiltration rate (mm / min); β is the attenuation coefficient (min -1 ), depending on soil properties.

[0105] The above parameters f0, f c , β are Horton infiltration parameters, which can be determined from the land cover type data of the study area.

[0106] S202, Design of confluence model: After rainwater forms on the ground, it flows from high altitude to low altitude and finally discharged through the drain. The governing equation of rainstorm inundation simulation is the two-dimensional shallow water equation (2D SWE);

[0107]

[0108] Where U = [hu x hu y h] T The vector representing the conserved quantity is composed of the water depth h and the flow rate per unit width in the x direction u. x flow rate per unit width in h and y directions u y h composition; u x and u yrepresent the depth-averaged flow velocities in the x and y directions, respectively.

[0109] The flux F in the x and y directions x and F y , is given by:

[0110]

[0111] Where g is the acceleration due to gravity.

[0112] The source terms on the right side of formula (5) include net rainfall rate, slope gravity and ground friction, which are expressed as:

[0113]

[0114] Among them, q, f, and w are precipitation rate, infiltration rate, and drainage rate respectively; z b is the ground elevation, which is used to model the slope source term; n is the Manning resistance coefficient, which is used to model the surface friction term.

[0115] By discretizing time and space, the two-dimensional SWE can be solved numerically. At each time interval, the following four steps are calculated for each element:

[0116] 1) Precipitation calculation;

[0117] 2) Infiltration calculation;

[0118] 3) Drainage calculation;

[0119] 4) Confluence calculation.

[0120] The first three steps are used to calculate the net rainfall rate, where the precipitation rate is determined by equations (2) and (3), and the infiltration rate is determined by equation (4). The drainage calculation uses the equivalent infiltration principle to make a generalized estimate of the drainage rate of the urban underground pipe network. Specifically, the drainage rate w is generally determined according to the actual design standard.

[0121] It should be noted that when calculating the precipitation, infiltration and drainage terms at the beginning of each time interval dt, the net increase in water depth of each grid is represented by max{(qfw)dt,-h} to maintain mass conservation and ensure that the absolute water level is not lower than the surface elevation.

[0122] For the confluence calculation, the finite volume method is used to solve the fluid dynamics equations. The reconstruction is performed using the piecewise linear method (PLM) with the MinMod slope limiter to achieve second-order spatial accuracy. The upwind center scheme is used as an approximate Riemann solver for the numerical fluxes at the grid interfaces. The time update uses an explicit forward Euler method with a variable time step, and the time interval dt is adaptively determined by the Courant-Friedrichs-Lewy (CFL) criterion to ensure numerical stability.

[0123] S3, constructing a model to simulate the flooding of outdoor equipment and indoor equipment;

[0124] Design equipment flooding module: The urban power distribution system consists of underground cables, substations, switch cabinets, distribution transformers, etc. Based on literature review and disaster data collected on site, the vulnerable components of the urban power distribution system under waterlogging conditions are mainly distribution nodes, including various distribution substations, cabinets, transformers, etc. Therefore, distribution nodes are identified as vulnerable components.

[0125] From this step on, the following model uses a fixed time step, and the duration of a time slot is Δt. Let t = 0 represent the time slot when the rainstorm starts, and t = T r represents the time slot when the rainstorm ends. It represents the set of time slot indicators during the rainstorm process. Considering that the equipment related to the power distribution node may be located outdoors or indoors, models are established to simulate the flooding process of outdoor equipment and indoor equipment respectively.

[0126] S301, Outdoor Flooding Model: In the face of an impending flood, one of the most effective strategies implemented by power companies is to cut off power to vulnerable nodes in the flood area in advance. Although this strategy will cause some power outages, it ultimately shortens the recovery time by avoiding the most serious damage. Therefore, if the rain depth at outdoor node i exceeds the critical flood control standard threshold of the device The node is shut down urgently and nearby switches are disconnected to isolate the fault.

[0127] make represents the set of outdoor flooded distribution nodes, where h i (t) is the water level at outdoor node i at time t; represents a collection of distribution nodes; represents the set of indoor nodes. For each flooded outdoor node i, the rain depth at its location exceeds The time slot is recorded as In addition, through the drainage simulation of the urban pipe network, the time slot of rainwater retreating from outdoor node i is recorded as

[0128] S302, indoor flooding model: when the power distribution facilities are located indoors, the indoor flooding depth is calculated as a function of the outdoor water depth;

[0129] When the outdoor water depth exceeds the indoor water depth, the calculation formula for rainwater flowing into the building is:

[0130]

[0131] Where Q is the flow rate of rainwater into the building (m 3 / s); C d is the flow coefficient set to 1 in the present invention; L is the width of the building door expected to be flooded, set to 0.75 m; h and h in Respectively represent the height of outdoor and indoor water surface (m).

[0132] The indoor water depth can be updated by:

[0133] h in (t+1)=h in (t)+Q(t)Δt / S in (9)

[0134] Among them, S in is the building area (m 2 ), which can be calculated based on the building boundary polygon.

[0135] Similarly, let represents the set of distribution nodes with indoor water accumulation, where h in,i (t) is the indoor water depth of the building where node i is located at time t, and the time slot corresponding to the indoor water accumulation is also recorded as

[0136] Here, the set of flooded distribution nodes can be recorded as Introduce a binary variable η i,t , which is used to indicate whether node i is normal at time t, that is, not flooded. For nodes where the rainwater does not rise above the critical water level, there is η i,t =1.

[0137] For the flooded nodes, their status during the rainstorm can be determined by the following formula:

[0138]

[0139] in, Represents the indicator function.

[0140] S4, modeling the mobile resource routing problem under urban waterlogging as a time-dependent vehicle routing problem;

[0141] Design of road flooding module: The road network is represented as a directed graph in Represents the set of road network nodes containing the locations of distribution nodes (i.e. ), ε represents the set of road arcs between different adjacent nodes in the road network.

[0142] In the context of flood disasters, the travel time of MER between different locations is affected by the flooded road conditions; usually, after the rainstorm ends, the travel time decreases monotonically with the departure time. This means that the mobile resource routing problem under urban flooding can be modeled as a time-dependent vehicle routing problem.

[0143] During the rainstorm, MER will be on standby at the warehouse to ensure the safety of the rescue workers. After the rainstorm, MER will set out to perform the mission; Represents the time slot indicator set after the rainstorm, where T r represents the time slot when the rainstorm ends, T max Indicates the maximum simulation time.

[0144] S401, establishing a flooded road traffic model;

[0145] According to existing research, the relationship between storm water depth and vehicle speed is expressed as:

[0146]

[0147] in, is the vehicle speed of the road section (u, v) at time t (km / h); is the normal vehicle speed (km / h) of the road section (u,v); h uv (t) is the maximum water depth (m) of the road section (u, v) at time t. When road flooding occurs, especially when the water depth exceeds 0.3m, the possibility of vehicle stalling increases greatly and the road is considered impassable.

[0148] S402, define discrete arrival time function: In order to adapt to the modeling framework of the space-time network, the present invention provides a definition of discrete arrival time function (ATF).

[0149] Definition 1 For The discrete arrival time function is defined as a function And satisfy:

[0150] 1.a non-decreasing;

[0151] 2.a(t)≥t(t=T r ,T min +1,…,T max ).

[0152] The arrival time function a describes the arrival time a(t) depending on the departure time t, where a(t)-t is the required journey time. For each road segment (u, v) and departure time The definition is as follows:

[0153]

[0154] in, and The ratio of can be obtained by formula (11); is the normal travel time of the road section (u, v), which can be obtained through the map application; Δt is the duration of each time slot; · is the rounding up function.

[0155] This function allows the MER to wait for the water on the road to recede before setting off, thus satisfying the first-in, first-out characteristic. In addition, for each road network node and time Definition vv (t) represents a stay at site v for one time slot.

[0156] The definition is as follows:

[0157] a vv (t):=t+1 (13)

[0158] S403, constructing a time-dependent space-time network: establishing a space-time coordinate system with time index as the horizontal coordinate and road network vertex as the vertical coordinate. The point set in the space-time coordinate system is represented by the Cartesian product Considering the time dependence of MER routing and combining the concept of arrival time function, the definition of space-time arc in the space-time coordinate system is given.

[0159] Definition 2 Given a road network and a set of time slots A spacetime arc in a spacetime network is defined as a To the top The arc of , and satisfies:

[0160] 1. (u, v)∈ε or u=v;

[0161] 2.a uv (t) = τ, where a is defined by equations (12)-(13);

[0162] Let this space-time arc be (uv,t).

[0163] From Definition 2, we can see that there are two types of spacetime arcs. The first type is the transfer arc. The second is the parking arc The set of space-time arcs in the space-time network is

[0164] Based on the concept of space-time arcs, the constraints related to post-disaster vehicle routing can be expressed as Equations (14)-(16). Introducing the binary variable x m,uv,tIndicates whether mobile resource m is on the space-time arc (uv, t). Spatial uniqueness constraint (14) constrains any MER to appear on only one arc in each time slot. Traffic flow continuity constraint (15) requires that the in-degree of each intermediate vertex of each MER in the space-time network is equal to the out-degree. Constraint (16) indicates that when the rainstorm ends, the MER leaves its warehouse site. Set off.

[0165]

[0166] It is worth noting that the above constraints (14)-(16) do not restrict the specific routes between the start-end pairs. In the context of urban flooding, the proposed model can combine all road arcs between the start-end and the end point, and automatically determine the optimal path under the current road network flooding conditions in combination with the objective function.

[0167] S5. Construct a mixed integer linear programming model as the distribution system response model, use elasticity metrics to quantify the elasticity of the distribution system, and realize the elasticity assessment of urban rainstorm waterlogging disasters.

[0168] Design system response module: When a component in the distribution system is flooded and out of service, emergency measures will be implemented to reduce the impact of the fault on the system and restore the system function to normal. Considering various fault handling measures such as network reconstruction (fault isolation and load recovery), drainage, equipment repair, and emergency power supply access, the distribution system response model is expressed as a mixed integer linear program (MILP) model.

[0169] Table 1 System corresponding module symbol table

[0170]

[0171]

[0172]

[0173] S501. Network reconstruction modeling: Network reconstruction is used to isolate faults and restore loads in non-faulty areas. Since the switch operation is completed by the distribution automation device, network reconstruction can be performed during and after heavy rain. Represents the time slot index set of the entire process. The distribution system network reconstruction needs to meet the following operating constraints:

[0174] a) Fault propagation constraints:

[0175] When a distribution node is flooded, the nearest switch is operated to isolate and cut off the power supply to the flooded node. The inevitable disadvantage of this measure is that due to the limited number of switch configurations in the distribution system, the power outage may extend to other areas that are not affected by the flood. This extended power outage characteristic of the distribution system is modeled as:

[0176]

[0177] Among them, i,t Indicates whether node i is located in an area that needs to be isolated at time t. Constraint (17) indicates that flooded nodes must be isolated. For a node that is not affected by flooding, if none of its neighboring nodes need to be isolated, then it does not need to be isolated either, as shown in (18). Conversely, if any of its neighboring nodes needs to be isolated, then it also needs to be isolated, as shown in (19). The nonlinear terms in equations (18)-(19) can be simply linearized, and here M=1 is set.

[0178] b) Topological radiation constraints:

[0179] Constraint (20) limits the connection state of the distribution line, that is, a distribution line can only be switched if it is equipped with a switch. Constraints (21)-(24) are radial constraints for the distribution system operation topology based on the maximum density of the graph.

[0180]

[0181] c) Power flow equation:

[0182] A linearized branch power flow model is used to solve the distribution system power flow, which includes the node active and reactive power balance equations (25)-(26) and the voltage drop calculation equation (27).

[0183]

[0184] d) System security constraints:

[0185] Constraints (28) and (29) limit the transmission capacity of the distribution line. Constraint (30) represents the node voltage amplitude limit.

[0186]

[0187] e) Load recovery constraints:

[0188] Constraint (31) limits the picking state of the power load, that is, when its associated node is in the fault area that needs to be isolated, the load cannot be restored. Constraint (32) means that during the restoration process after the rainstorm, once the load is restored, it will not be disconnected again to avoid frequent user power outages.

[0189]

[0190] S502, drainage modeling: using y i,t To indicate whether the rainwater at the flooded distribution node i has receded or been drained at time t. For outdoor flooded nodes, by solving (5), we can obtain the number of time slots required for rainwater to recede from outdoor node i: For the flooded distribution room, a drainage vehicle is deployed to drain the indoor water. Constraint (34) states that when the cumulative residence time of the drainage vehicle at the indoor flooded node i reaches When , the water is completely drained, and the time required for indoor drainage can be estimated by the indoor water depth and the drainage capacity of the drainage vehicle. Constraint (35) represents the state of the flooded node after drainage.

[0191]

[0192] S503, Equipment repair modeling: After the rainstorm, repair personnel are deployed to the site to repair the equipment. In order to describe the time spent by the repair personnel on fault clearance, φ m,i,t To indicate whether worker m is repairing equipment at node i at time t. According to equations (36) and (37), equipment repair is only performed when the location of the damaged facility is accessible and the water has receded or been completely drained; after worker m starts repairing the damaged node i, after time slot The fault is cleared and its available state is η i,t Change; Constraint (38) states that only when person m spends When the damaged node i is repaired in time slots, there is z m,i,t =1; Constraint (39) specifies the state of the repair node. Constraint (40) indicates that each damaged node can only be repaired by one of the repair personnel. The availability state of the flooded nodes after the rainstorm ends can be derived from equation (41).

[0193]

[0194]

[0195] S504. Emergency power access modeling: Distributed backup power is usually used to quickly restore power to users before damaged equipment is repaired. During heavy rain, the output of distributed backup power is limited by (42) and (43), which means that power can only be connected if the site is not flooded. It should be noted that if node i is not equipped with any power supply, then

[0196]

[0197] After the rainstorm ends, MEG is deployed to provide power support for the power outage nodes in the non-fault area. m,i,t Indicates whether MEG m is connected to node i at time t. Constraints (44)-(45) indicate that MEG can only be connected to reachable and undamaged distribution nodes. Constraint (46) limits the number of allowed MEG connections. When MEG is connected to the grid after the rainstorm, the power output is limited by equations (47)-(50).

[0198]

[0199] S505, collaborative optimization model: the overall response model of the distribution system under waterlogging is modeled as a collaborative optimization model;

[0200]

[0201] sta) fault propagation constraints (10), (17)-(19);

[0202] b) Topological radiation constraints (20)-(24);

[0203] c) Flow equations (25)-(27);

[0204] d) System security constraints (28)-(30);

[0205] e) Load recovery constraints (31)-(32);

[0206] f) Dynamic routing constraints (14)-(16);

[0207] g) Flood discharge constraints (33)-(35);

[0208] h) Equipment repair constraints (36)-(41);

[0209] i) Power access constraints (42)-(50);

[0210] Among them, C i Represents the number of users connected to node i. The objective function (51) is to minimize the number of households during power outages and the travel time of mobile emergency resources. The relative weights of the two objective items can be adjusted by the parameter ε, which is set to a small value to ensure that the first objective item dominates. The first item in (51) represents the cumulative sum of the duration of power outages for users. Therefore, the distribution system response model aims to reduce power outages for users and speed up recovery, which is suitable for distribution system resilience assessment.

[0211] S506. Resilience index design: The IEEE resilience index is defined as the ratio of the number of users with power outages of more than 12 hours to the total number of user interruptions, including user interruptions that are automatically restored or avoided through technologies such as smart switch operation and microgrids. Based on the solution results of the distribution system response model (51), the number of continuous user interruptions and the number of avoided user interruptions are counted and recorded as C SI and C AI .

[0212] To more accurately quantify the rate of recovery of the distribution system within the first 12 hours, a parameterized IEEE resilience metric was used:

[0213]

[0214] Wherein, parameter t represents the duration of the power outage of interest; is an indicator function to determine whether the node power outage duration exceeds time t; R t is a parameterized elasticity index.

[0215] In yet another embodiment of the present invention, a system for assessing the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters is provided. The system can be used to implement the above-mentioned method for assessing the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters. Specifically, the system for assessing the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters includes a rainstorm module, a surface runoff module, an equipment flooding module, a road flooding module, and a system response module.

[0216] Among them, the rainstorm module builds a rainstorm model based on rainfall and rainfall process to obtain the time series rainfall intensity;

[0217] The surface runoff module builds a runoff model and a confluence model, determines the change process of the surface water depth according to the obtained time-series rainfall intensity, and obtains the surface water depth simulation result; the equipment flooding module builds a model based on the surface water depth simulation result to simulate the outdoor equipment flooding and indoor equipment flooding process, and obtains the distribution system disaster scenario and the damaged equipment set;

[0218] The road flooding module builds a road network model based on the distribution system disaster scenario and the damaged equipment set, and models the mobile resource path problem under urban waterlogging as a time-dependent vehicle routing problem; the system response module builds a mixed integer linear programming model as a distribution system response model based on the time-dependent vehicle routing problem, and quantifies the elasticity of the distribution system using elasticity metrics. t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

[0219] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the elasticity assessment method of the power distribution system to cope with urban rainstorm and waterlogging disasters, including:

[0220] A rainstorm model is constructed based on the rainfall amount and rainfall process to obtain the time-series rainfall intensity; a runoff generation model and a confluence model are constructed to determine the change process of the surface water depth according to the obtained time-series rainfall intensity, and obtain the surface water depth simulation result; a model is constructed based on the surface water depth simulation result to simulate the flooding process of outdoor equipment and indoor equipment, and obtain the distribution system disaster scenario and damaged equipment set; a road network model is constructed based on the distribution system disaster scenario and damaged equipment set, and the mobile resource path problem under urban waterlogging is modeled as a time-dependent vehicle routing problem; a mixed integer linear programming model is constructed based on the time-dependent vehicle routing problem as a distribution system response model, and the elasticity metric index is used to quantify the elasticity of the distribution system. Based on the obtained elasticity index R t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

[0221] See also Figure 4 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the elastic evaluation system for the power distribution system to cope with urban rainstorm and waterlogging disasters in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0222] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0223] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0224] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0225] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0226] See also Figure 5 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0227] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 6 Follow the steps shown in .

[0228] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0229] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0230] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0231] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0232] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0233] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters in the above-mentioned embodiment; the processor may load and execute the following steps:

[0234] A rainstorm model is constructed based on the rainfall amount and rainfall process to obtain the time-series rainfall intensity; a runoff generation model and a confluence model are constructed to determine the change process of the surface water depth according to the obtained time-series rainfall intensity, and obtain the surface water depth simulation result; a model is constructed based on the surface water depth simulation result to simulate the flooding process of outdoor equipment and indoor equipment, and obtain the distribution system disaster scenario and damaged equipment set; a road network model is constructed based on the distribution system disaster scenario and damaged equipment set, and the mobile resource path problem under urban waterlogging is modeled as a time-dependent vehicle routing problem; a mixed integer linear programming model is constructed based on the time-dependent vehicle routing problem as a distribution system response model, and the elasticity metric index is used to quantify the elasticity of the distribution system. Based on the obtained elasticity index R t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

[0235] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0236] The improved IEEE 33-bus power distribution system was used as the test system, and ANUGA 3.1.9 and Python 3.10.14 were used to model and solve the 2D SWE. Gurobi 11.0.0 and Python 3.11.5 were used to model and solve the involved MILP.

[0237] For MER, two repair personnel, one emergency generator and one emergency drainage vehicle are considered; the fault clearing time for each fault node is 90 minutes; the capacity of the emergency generator is set to 2MVA; the drainage capacity of the emergency drainage vehicle is set to 500m 3 / h. Test the switch configuration and distributed backup generators in the distribution system such as Figure 1 As shown in the figure. The number of low-voltage users at each distribution node is estimated based on the active load of the node, assuming that the active power of each household is 5kW. In the simulation of system response behavior, the time slot is set to Δt = 10min. Since the proposed model focuses on the recovery of the first 12 hours, the maximum simulation time is set to 12 hours after the end of the design rainstorm.

[0238] A design rainstorm with a return period of 50 years, a duration of 120 minutes, and a rain peak coefficient of 0.48 was generated as a simulated disaster scenario. Due to urban waterlogging caused by the rainstorm, 4 outdoor distribution nodes and 2 indoor distribution nodes were flooded. At the end of the rainstorm (t = 120 minutes), the total load loss was as high as 3415kW, 683 households experienced continuous power outages, and 60 households avoided power outages by forming microgrids with distributed power sources.

[0239] In order to illustrate the impact of equipment point flooding duration (factor A) and urban road flooding (factor B), three cases were designed. The factors considered in each case are detailed in Table 2. Case I does not consider any restrictions related to rain and floods, that is, it is handled in the same way as the conventional fault recovery process, involving only the deployment of emergency repair personnel and emergency power generation vehicles. The results show that emergency repair personnel tend to give priority to repairing nodes or areas with a large number of users. The MER scheduling Gantt chart and system resilience curve are shown in Figure 2. Figure 3 For Case I, after the rainstorm disaster ends, it takes 7.5 hours to restore power supply to all users.

[0240] Table 2 Comparison of three cases considering different factors

[0241]

[0242]

[0243] However, repair work under flooding is subject to whether the water in the fault area has receded. When considering this factor in Case II, the repair order changes, and in some cases the repair personnel have to wait on the spot. For example, repair personnel 1 arrives at node 17 at t = 3.0h, but the emergency drainage vehicle needs to wait until t = 3.8h to drain the indoor water before starting repair work. Similarly, repair personnel 2 arrives at node 4 at t = 3.2h, but equipment repair can only begin after the water recedes at t = 5.0h. In this case, it takes 8.8h for all users to regain power after the rainstorm ends.

[0244] In fact, the accessibility of the fault point is also a crucial factor to be considered. After considering this factor in Case III, the repair order changes again, and the routes of the emergency drainage vehicle and the emergency power generation vehicle also change. Figure 2 The roads shown were flooded, and mobile resources still had to wait in the vehicle depot before departure. In addition, the average driving time and on-site standby time increased by 189% compared to Case I. The entire post-disaster recovery process lasted 10.5 hours, 1.4 times that of Case I.

[0245] In summary, the present invention provides a method and system for evaluating the resilience of a power distribution system in response to urban rainstorm and waterlogging disasters. The method and system take into account the characteristics of urban rainstorm and waterlogging disasters and the response behavior of the power distribution system during the disaster resistance and post-disaster recovery stages, and take into account the impact of equipment flooding time and road flooding on the response and resilience indicators of the power distribution system. The resilience assessment of the power distribution system under rainstorm and flood disasters is more comprehensive and the assessment results are more accurate.

[0246] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0247] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0248] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0249] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0250] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0251] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0252] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0253] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0254] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0255] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0256] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the resilience of power distribution systems to urban rainstorm and flooding disasters, characterized in that: The following steps are involved: A rainstorm model was constructed based on rainfall and rainfall process to obtain the time series rainfall intensity; Construct runoff generation model and runoff confluence model, determine the change process of surface water depth according to the obtained time series rainfall intensity, and obtain the surface water depth simulation result; Based on the surface water depth simulation results, a model was built to simulate the flooding of outdoor equipment and indoor equipment, and the disaster scenario of the distribution system and the collection of damaged equipment were obtained; A road network model is constructed based on the power distribution system disaster scenario and the damaged equipment set, and the mobile resource path problem under urban waterlogging is modeled as a time-dependent vehicle routing problem; Based on the time-dependent vehicle routing problem, a mixed integer linear programming model is constructed as the distribution system response model. The elasticity metric index is used to quantify the elasticity of the distribution system. Based on the elasticity index H t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

2. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 1 is characterized in that: The specific construction of the rainstorm model based on rainfall and rainfall process is as follows: An intensity-duration-frequency curve was constructed to obtain the average rainfall intensity. The Chicago rain type was used to design the rainfall process. The peak moment of the rainfall process was represented by the rain peak coefficient. The rainfall duration was divided into two periods before and after the peak. The instantaneous rainfall intensity before and after the peak was obtained based on the average rainfall intensity.

3. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 2 is characterized in that: The pre-peak and post-peak instantaneous intensities q1 and q2 of the rainstorm process are: Among them, t1 and t2 represent the time before and after the peak of the rainstorm duration respectively; r represents the peak position coefficient of the design rainstorm process derived from historical rainstorm data; A, C, b, n r is the coefficient related to the city where the study area is located; P represents the return period.

4. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 1, characterized in that: The flow generation model is constructed as follows: f(t)=f c +(f0-f c )e -βt Where f(t) is the infiltration rate at time t; f0 is the initial infiltration rate; f c is the stable infiltration rate of soil; β is the attenuation coefficient; The confluence model is as follows: Among them, U represents the vector of conserved quantity; u x and u y represent the average velocity of water depth in the x-direction and the y-direction respectively; F x and F y They represent the flux in the x and y directions respectively; S represents the source term.

5. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 1, characterized in that: The model is constructed to simulate the flooding of outdoor equipment and indoor equipment as follows: The outdoor flooding model is as follows: If the rain depth at outdoor node i exceeds the critical flood control standard threshold of the device Shut down the node urgently and disconnect nearby switches to isolate the fault. represents the set of outdoor flooded distribution nodes, h i (t) is the water level at outdoor node i at time t; represents a collection of distribution nodes; represents the set of indoor nodes; for each flooded outdoor node i, the rain depth at its location exceeds The time slot is recorded as Through the drainage simulation of the urban pipe network, the time slot of rainwater retreating from outdoor node i is recorded as The indoor flooding model is as follows: When the power distribution facilities are located indoors, the indoor flooding depth is calculated as a function of the outdoor water depth; let represents the set of distribution nodes with indoor water accumulation, where h in，i (t) is the indoor water depth of the building where node i is located at time t, and the time slot corresponding to the indoor water accumulation is also recorded as The set of flooded distribution nodes is denoted as Introducing binary variable η i，t Indicates whether node i is normal at time t. For nodes where the rainwater has not risen above the critical water level, η i，t =1; the status of the flooded nodes during the rainstorm is as follows: in, Represents the indicator function.

6. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 5, characterized in that: When the outdoor water depth exceeds the indoor water depth, the rainwater flowing into the building is calculated as follows: Where, Q is the flow rate of rainwater into the building; C d is the flow coefficient set to 1 in the present invention; L is the width of the building door expected to be flooded; h and h in Respectively represent the height of outdoor and indoor water surface; Indoor water depths updated as follows: h in (t+1)=h in (t)+Q(t)Δt / S in Among them, S in is the building area.

7. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 1, characterized in that: The mobile resource routing problem under urban waterlogging is modeled as a time-dependent vehicle routing problem. Specifically, a flooded road traffic model is established based on the storm water depth and vehicle speed; Define discrete arrival time functions to meet the needs of the spatiotemporal network modeling framework; A space-time coordinate system is established with the time index as the horizontal coordinate and the road network vertex as the vertical coordinate to reflect the spatial changes of mobile resources in the road network at different times after the end of the rainstorm; based on the discrete arrival time function, the concept of space-time arc in the space-time coordinate system is defined, and the constraints related to post-disaster vehicle routing are expressed under the concept of space-time arc.

8. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 7, characterized in that: The relationship between storm water depth and vehicle speed is: in, is the vehicle speed of the road section (u, υ) at time t; is the normal vehicle speed on the road section (u, υ); h uυ (t) is the maximum water depth of the road section (u, υ) at time t; for Define the discrete arrival time function as a function And satisfy: a is non-decreasing; a(t)≥t(t=T r , T min +1,…,T max ); Given a road network and a set of time slots Define the space-time arc in the space-time network as the arc from vertex (u, t) to vertex arc, and satisfies: (u,υ)∈ε or u=υ; a uυ (t) = τ; let this space-time arc be (uυ, t) The constraints are as follows: Among them, x m，uυ，t is whether the mobile resource m is on the space-time arc (uυ, t), is whether the mobile resource m is on the space-time arc (uυ, τ), is the set of space-time arcs in the space-time network, t / τ is the time slot index, is the time slot index set after the rainstorm, is the time slot corresponding to the end of the rainstorm, is the maximum simulation time of the model, A collection of mobile emergency resources. is the set of road network nodes, is the warehouse point of mobile resource m, Is the mobile resource m in the space-time arc superior.

9. The method for evaluating the resilience of a power distribution system to cope with urban rainstorm and waterlogging disasters according to claim 1, characterized in that: A mixed integer linear programming model is constructed as the distribution system response model, and the elasticity of the distribution system is quantified using elasticity metrics to achieve elasticity assessment of urban rainstorm waterlogging disasters. Specifically: The overall response model of the distribution system under waterlogging is modeled as a collaborative optimization model. The proportion of users with power outages of more than 12 hours to the total number of user interruptions is defined as the IEEE resilience index, including user interruptions automatically restored or avoided through intelligent switch operation and microgrid technology. Based on the solution results of the collaborative optimization model, the number of continuous user interruptions and the number of avoided user interruptions are counted to determine the parameterized resilience index R. t as follows: Elasticity index R t as follows: Wherein, parameter t represents the duration of the power outage of interest; It is an indicator function to determine whether the duration of node power outage exceeds time t; The collaborative optimization model is as follows: in, represents a set of time slot indices, represents the set of nodes in the power distribution system, δ i，t Indicates whether the load at node i is restored at time t, C i represents the number of users connected to node i, Δt represents the fixed time interval of each time slot, and ε represents a relatively small positive number. Represents a collection of mobile emergency resources, represents the set of space-time arcs in the space-time network, a uυ (t) represents the arrival time of the route (u, υ) at time t, x m，uυ，t Indicates whether the mobile resource m is on the space-time arc (uυ, t).

10. A resilience assessment system for power distribution systems in response to urban rainstorm and flooding disasters, characterized in that: include: The rainstorm module builds a rainstorm model based on rainfall and rainfall process to obtain the time series rainfall intensity; The surface runoff module builds a runoff model and a confluence model, determines the change process of the surface water depth according to the obtained time-series rainfall intensity, and obtains the surface water depth simulation result; the equipment flooding module builds a model based on the surface water depth simulation result to simulate the outdoor equipment flooding and indoor equipment flooding process, and obtains the distribution system disaster scenario and the damaged equipment set; The road flooding module builds a road network model based on the distribution system disaster scenario and the damaged equipment set, and models the mobile resource path problem under urban waterlogging as a time-dependent vehicle routing problem; the system response module builds a mixed integer linear programming model as a distribution system response model based on the time-dependent vehicle routing problem, and quantifies the elasticity of the distribution system using elasticity metrics. t Realize the resilience assessment of urban rainstorm and waterlogging disasters.

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