A method and system for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation

Abstract

This invention discloses a method and system for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation. It uses the component characteristics, network characteristics, and fault probabilities of power distribution network equipment as labels, and constructs a three-dimensional spatial map to partition the equipment based on their vulnerability levels. The scheduling of flood prevention personnel is coupled with the obtained vulnerability partitioning to construct a decision optimization model for pre-disaster flood prevention and control of the power distribution network. An optimization solver is used to solve the obtained decision optimization model to obtain a pre-disaster flood prevention and control scheme for the power distribution network. This invention can significantly reduce the risk of power outages in urban power distribution networks during rainstorms and floods, thereby reducing social and economic losses.

Description

Technical Field

[0001] This invention belongs to the field of disaster prevention and mitigation technology for power distribution systems, specifically relating to a method and system for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation. Background Technology

[0002] In recent years, with the occurrence of extreme weather events, extreme rainstorm disasters have become more frequent and their harm is extremely severe. Urban flooding caused by extreme rainstorms occurs when the amount of rainfall in a short period of time exceeds the capacity of the drainage network, resulting in water accumulation. This not only severely affects underground power distribution rooms, causing some power distribution equipment to malfunction due to water immersion, but also damages the insulation of electrical equipment, triggers relay protection device failures, and may even lead to the shutdown of the entire substation. Urban flooding caused by extreme rainstorms seriously affects the stable operation of the power distribution network, and the large-scale power outages caused by them can result in huge economic losses and even casualties. Therefore, improving the flood prevention capabilities of the power distribution network is extremely important.

[0003] Before floods, existing research proposed a three-level optimization strategy, which involves identifying vulnerable distribution lines, determining the set of out-of-service distribution lines, and reducing the cost of restoring power supply based on load priority and the set of damaged lines, thereby enhancing the resilience of the distribution network in the face of extreme disasters. However, this strategy failed to address the specific disaster-causing characteristics of floods. During floods, it is crucial to obtain timely information on the damage and faults in the distribution network, make real-time decisions, and implement relevant emergency strategies to minimize load losses during the disaster. Existing research has constructed an emergency management framework for urban distribution networks during floods based on emergency management theory. This framework allows for dynamic updates of disaster information and real-time adjustments to emergency repair plans, enabling rapid and efficient emergency repairs with the joint support of an integrated emergency material storage, inspection, and distribution management system and an emergency repair command system. After floods, research has developed an active distribution network fault recovery method and system. This system utilizes smart soft switches (Soft Open Point, SOP) and distributed generation to restore power to affected loads, maximizing load recovery.

[0004] Current research proposes methods to address flood prevention in power distribution networks from three different stages: before, during, and after extreme disasters. However, these methods are remedial during and after disasters and still have certain limitations, such as the deployment and allocation of repair personnel, the updating of traffic information, and the speed and route selection of repair vehicles. Therefore, if scientific preventive measures are taken to pre-deploy power distribution equipment and if weak points are identified and flood prevention work is allocated in advance, limited resources can be distributed to vulnerable nodes. This will not only reduce power outage losses caused by flooding but also maximize flood prevention capabilities. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation, in order to address the shortcomings of the prior art. This method is used to solve the technical problem that it is difficult to identify weak points in the power distribution network under rainstorm and flood disasters, and it is difficult to take scientific preventive measures to deploy power equipment in advance.

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

[0007] A method for pre-disaster flood prevention deployment in power distribution networks based on resource optimization allocation includes the following steps:

[0008] Using the component characteristics, network characteristics, and fault probability of power distribution network equipment as labels, a three-dimensional spatial map is constructed to partition the equipment based on its weak points.

[0009] By coupling the dispatch of flood control personnel with the obtained weak zone divisions, a decision optimization model for pre-disaster flood control deployment of the power distribution network is constructed.

[0010] The decision optimization model for pre-disaster flood prevention and control of the power distribution network is solved using an optimization solver to obtain the pre-disaster flood prevention and control scheme for the power distribution network.

[0011] Preferably, the method of constructing a three-dimensional spatial map to partition the equipment based on its weak points is as follows:

[0012] Model the component characteristics of power distribution network equipment, including load, expected repair time, and expected damage cost;

[0013] The network characteristics of distribution network equipment are modeled, including the degree centrality and betweenness centrality of nodes;

[0014] The solution is divided into two categories based on whether the node is a substation, to obtain the failure probability of distribution network equipment under urban flooding disaster.

[0015] A weak point identification model for the distribution network is constructed based on the component characteristics, network characteristics, and fault probabilities of distribution network equipment. Each distribution node is placed in a three-dimensional diagram according to its characteristic values, and the coordinate point information represents the component characteristics, network characteristics, and fault probability.

[0016] Preferably, the component characteristic CF of distribution node i i Expressed as:

[0017]

[0018] Where a, b, and c represent the weighting coefficients of various component characteristics, a + b + c = 100%; ω i P represents the weight coefficient of node i; i NThe load at node i; DC i Let be the expected damage cost of node i; Let be the expected repair time for node i.

[0019] Preferably, the network characteristic NF of distribution node i i Expressed as:

[0020]

[0021] Where u and v are the weight coefficients of various network features; dc i Let bc represent the degree centrality of node i. i Let i be the betweenness centrality of node i.

[0022] Preferably, the failure probability P of power distribution network equipment under urban flooding disasters i (t) is:

[0023]

[0024] Where T represents the study duration, λ i (t) represents the failure rate of distribution node i. Δ t is the time step.

[0025] Preferably, the decision optimization model for pre-disaster flood prevention and control deployment of the power distribution network is specifically constructed as follows:

[0026]

[0027] Where z represents whether the distribution node is protected (a 0-1 variable); S represents the set of fault scenarios for the distribution node; y s x is a generalized variable representation of the operating state of the distribution network under disaster scenario s; i,s f() represents whether node i is flooded in scenario s; f() is the distribution network loss function.

[0028] Preferably, the decision optimization model for pre-disaster flood prevention and control in the power distribution network is divided into two stages. The first stage focuses on the protection measures for pre-disaster nodes, and the constraints include flood prevention resource scheduling constraints and system and component operation constraints. The model for the first stage is as follows:

[0029]

[0030] Among them, Q s () is the tracing function that provides the optimal value for the second-stage optimization problem in scenario s; N For the set of distribution nodes; z i Z represents whether node i is protected (a 0-1 variable); Z is the upper limit of the number of protected nodes.

[0031] The second-stage optimization model focuses on the load shedding of the distribution network.

[0032]

[0033] in, ωi The load priority weight at node i; The restored state of node i in scene s; P i N The active load of node i.

[0034] Preferably, the constraints on flood control resource allocation are:

[0035] The implementation time for flood control measures is set at 12 hours before the disaster, simulating the spatial uniqueness of flood control teams within a limited time, with the following constraints:

[0036]

[0037]

[0038] Where, x n,i,t Indicates whether the flood control team n is located at node i at time t; τ i The timeframe for implementing flood control measures at node i;

[0039] The time constraints for implementing temporary flood protection at substations and other types of nodes are as follows:

[0040]

[0041] Where, d i N represents the maximum possible water depth at node i during the development of an urban flooding disaster; team The number of members in the flood control team; For the set of substation nodes; It is the set of nodes in the distribution network excluding substations;

[0042] To describe the state transitions of nodes, a 0-1 variable y is introduced. n,i,t Ensure that only one team completes the protection measures for node i and leaves after a certain period of time, and that each node undergoes at most one state transition; specific constraints are as follows:

[0043]

[0044] Where ε is a sufficiently small positive number; x n,i,t Indicates whether the flood control team n is located at node i at time t; y n,i,t Indicates whether node i was repaired by flood control team n at time t;

[0045] The coupling constraints between flood control resource allocation and weak points in the power distribution network are as follows:

[0046]

[0047] in, Indicates whether node i within the stable region is protected against flooding; To identify the set of nodes within the stable region of the vulnerability identification model; This indicates whether the coordinate node k outside the stable and weak regions is protected; Indicates whether node j within the vulnerable area is protected against flooding; v is the set of nodes excluding the stable and weak regions; v is the set of nodes within the weak region.

[0048] Preferably, the system and component operating constraints are as follows:

[0049] Radial topological constraints:

[0050]

[0051] in, β is a non-negative, continuous auxiliary variable. ij,s The connection state of line (i,j) in scenario s; A collection of distribution network lines; For the set of distribution network nodes;

[0052] Linearized power flow constraints:

[0053]

[0054] in, Let (i,j) be the active power flow of line (i,j) in scenario s; Let (j,i) be the active power flow of line (j,i) in scenario s; Let i be the active power output of the power source at node i in scenario s. δ i,s P represents the recovery state of the load at node i in scenario s; i N Let i be the active load of node i; Let (i,j) be the reactive power flow of line (i,j) in scenario s; Let (j,i) be the reactive power flow of line (j,i) in scenario s; The reactive power output of the power source at node i in scenario s; Let v be the reactive load of node i; M be a sufficiently large positive number; i,s v is the square of the voltage magnitude of node i in scene s; j,s r is the square of the voltage amplitude of node j in scene s; ij / x ij Let (i,j) be the impedance of the line.

[0055] Line capacity constraints:

[0056]

[0057] in, This represents the upper limit of the apparent power of line (i,j);

[0058] Voltage amplitude constraint:

[0059]

[0060] in, This represents the lower limit of the voltage at node i; This represents the upper limit of the voltage at node i;

[0061] Power output constraints:

[0062]

[0063] in, Whether node i is available in scenario s; This represents the upper limit of the active power output of the power source at node i; A collection of distributed power sources in a power distribution network; This represents the upper limit of the reactive power output of the power supply at node i.

[0064] Constraints on the impact of waterlogging:

[0065]

[0066] Flood control constraints:

[0067]

[0068] Where, x i,s Whether node i is submerged in scene s; δ i,s This represents the recovery state of the load at node i in scenario s. ηi,s Whether node i is available in scenario s; β ij,s Let z be the connection state (0-1 variable) of line (i,j) in scenario s; i Whether node i is protected against flooding.

[0069] Secondly, embodiments of the present invention provide a pre-disaster flood prevention and control system for power distribution networks based on resource optimization allocation, comprising:

[0070] The partitioning module uses the component characteristics, network characteristics, and fault probabilities of power distribution network equipment as labels to construct a three-dimensional spatial map to partition the equipment based on its weakness.

[0071] The module is constructed to couple the dispatch of flood control personnel with the obtained weak point zoning, and to build a decision optimization model for pre-disaster flood control deployment of the power distribution network;

[0072] The deployment module uses an optimization solver to solve the decision optimization model of the pre-disaster flood prevention deployment of the power distribution network, and obtains the pre-disaster flood prevention deployment scheme of the power distribution network.

[0073] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for flood prevention and control of power distribution networks based on resource optimization allocation.

[0074] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for flood prevention and control of power distribution networks based on resource optimization allocation.

[0075] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for flood prevention and control of power distribution networks based on resource optimization allocation.

[0076] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for flood prevention and control of power distribution networks based on resource optimization allocation.

[0077] Compared with the prior art, the present invention has at least the following beneficial effects:

[0078] A pre-disaster flood prevention deployment method for distribution networks based on resource optimization allocation considers not only equipment failure probability but also component and network characteristics, which helps to fully identify weak points in the distribution network. A weak point identification model is proposed, which can identify nodes in weak areas and recognize important distribution equipment, assisting power companies in taking protective measures for critical nodes. Based on the weak point identification model, a stochastic optimization decision-making model for distribution network flood prevention is proposed. The load loss amount using the stochastic optimization decision scheme is significantly reduced compared to empirical flood prevention schemes. Finally, a scientifically sound flood prevention resource allocation scheme is obtained based on the proposed method, providing a decision-making basis for urban distribution network dispatching.

[0079] Furthermore, a three-dimensional spatial map is constructed to partition the equipment based on its weak points, thereby assisting the power company in taking protective measures for critical nodes.

[0080] Furthermore, a decision optimization model for pre-disaster flood prevention and control of the distribution network is constructed. The model considers an urban distribution network composed of nodes (i.e., different types of substations) and lines; it treats distribution nodes as vulnerable components of the urban distribution network under flood disasters; and it assumes that protected distribution nodes are no longer affected by flood disasters, making the stochastic optimization problem easier to handle.

[0081] Furthermore, the decision optimization model for flood prevention and control in the power distribution network before disasters is divided into two stages, which fully considers the uncertainty of disaster-affected nodes under flood disasters and improves the stability of flood prevention and control schemes.

[0082] Furthermore, the coupling constraint between flood control resource allocation and weak points ensures that flood control resources are prioritized for weak nodes and are not wasted on nodes within stable zones.

[0083] Furthermore, system and component operating constraints ensure the safe and stable operation of the power system.

[0084] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0085] In summary, this invention considers not only equipment failure probability but also the component characteristics and network characteristics of the equipment, which helps to fully identify weak points in the distribution network. It proposes a distribution network weak point identification model, which can identify nodes in weak areas, identify important distribution equipment, and assist power companies in taking protective measures for critical nodes. Based on the weak point identification model, a stochastic optimization decision-making model for distribution network flood prevention is proposed. The load loss when using the stochastic optimization decision-making scheme is significantly reduced compared to the load loss of empirical flood prevention schemes.

[0086] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0087] Figure 1 This is a graph showing the damage curve of the substation.

[0088] Figure 2 A time curve for substation repair;

[0089] Figure 3 This is a graph showing the probability of substation failures under urban flooding disasters.

[0090] Figure 4 A map showing the weak points of power distribution nodes under urban flooding disasters;

[0091] Figure 5 This is a schematic diagram illustrating the coupling between the power distribution network and the transportation network.

[0092] Figure 6 A visualization of the failure probability of a power distribution node;

[0093] Figure 7 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0094] Figure 8 This is a block diagram of a chip according to an embodiment of the present invention;

[0095] Figure 9 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0096] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0097] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0098] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0099] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0100] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. 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.

[0101] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0102] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0103] This invention provides a pre-disaster flood prevention deployment method for power distribution networks based on resource optimization allocation. It uses the component characteristics, network characteristics, and fault probabilities of power distribution network equipment as labels, and constructs a three-dimensional spatial map to partition the equipment based on its weak points. Then, it couples the scheduling of flood prevention personnel with the weak points partitions to build a decision optimization model for pre-disaster flood prevention deployment of power distribution networks, thereby improving the flood prevention capabilities of urban power distribution networks. Finally, it verifies and analyzes the model results through numerical examples.

[0104] Please see Figure 9 This invention discloses a pre-disaster flood prevention deployment method for power distribution networks based on resource optimization allocation, comprising the following steps:

[0105] S1. Identification of weak points in the power distribution network;

[0106] The failure probability of power distribution equipment under disasters is crucial for flood control deployment models. It is also necessary to take into account the component characteristics such as the load carried by the power distribution equipment and the network characteristics such as the betweenness centrality of the equipment nodes. The identification of weak points in the power distribution network under urban flooding disasters should fully consider the component characteristics, network characteristics and failure probability of the equipment.

[0107] S101, Component feature modeling of power distribution network equipment;

[0108] This includes load capacity, expected repair time, and expected damage costs. Substations with larger loads, longer expected repair times, and higher expected damage costs from torrential rains and flooding are more important to power companies.

[0109] Component characteristics CF of distribution node i i Expressed as:

[0110]

[0111] Where a, b, and c represent the weighting coefficients of various component characteristics, a + b + c = 100%; ωi P represents the weight coefficient of node i; i N The load at node i; DC i Let be the expected damage cost of node i; Let be the expected repair time for node i.

[0112] The importance of substation nodes in the distribution network is more prominent. Therefore, the expected damage cost and expected repair time of distribution nodes can be divided into two categories according to the node type.

[0113] 1) Substation node

[0114] The expected damage cost of a substation node is further expressed as:

[0115]

[0116] Among them, P i Let be the failure probability of node i; Let i be the damage percentage of node i; Let represent the cost price of node i. The damage rate and expected repair time of substation nodes are both related to the depth of flooding, as shown in the following relationship: Figure 1 and Figure 2 As shown.

[0117] 2) Ordinary nodes

[0118] Unlike substations, ordinary distribution nodes such as switching stations and ring main units are typically considered to be in a state of total failure if damaged in an extreme disaster. Therefore, the expected damage cost of ordinary nodes can disregard the proportion of equipment damaged; their expected damage cost DC... i Represented as:

[0119]

[0120] Among them, P i Let be the failure probability of node i; Let be the cost price of node i.

[0121] The expected repair time for a normal node can be further expressed as:

[0122]

[0123] in, The recovery time is when node i completely fails.

[0124] S102. Network characteristic modeling of distribution network equipment;

[0125] This includes the degree centrality and betweenness centrality of nodes. Nodes with higher degree centrality and betweenness centrality are more important to the network structure.

[0126] Network characteristics of distribution node i (NF) i Represented as:

[0127]

[0128] Where u and v are the weight coefficients of various network features, u + v100%; dc i Let bc represent the degree centrality of node i. i Let i be the betweenness centrality of node i.

[0129] Degree centrality and betweenness centrality are both commonly used centrality metrics in complex network research, typically used to measure the importance of a node in the global topology.

[0130] 1) Degree centrality

[0131] The degree of a node refers to the number of its neighboring nodes or edges; a higher degree indicates a more important node. The degree centrality of node i is dc. i This can be further expressed as:

[0132]

[0133] Where, k i Let be the degree of node i; n is the number of nodes in the network.

[0134] 2) Betweenness centrality

[0135] For the topology of a distribution network, there must exist at least one shortest path between each pair of nodes, such that the number of lines traversed by the path (unweighted graph) or the sum of line weights (weighted graph) is minimized. The betweenness centrality of a node describes the number of shortest paths passing through that node in the topology, and can reflect the mediating role that node plays in information transmission. The greater the betweenness centrality, the greater its connectivity role among nodes.

[0136] Betweenness centrality of node i (bc) i Represented as:

[0137]

[0138] in, σ st (i) This represents the number of nodes i that are passed through in the shortest path between node s and node t. σ st This represents the number of shortest paths between node s and node t.

[0139] S103. Equipment failure rate analysis under waterlogging conditions;

[0140] The failure probability of power distribution network equipment under urban flooding disasters can be solved by classifying the nodes into two categories based on whether they are substations.

[0141] 1) Substation node

[0142] The failure probability of substations varies significantly depending on the depth of flooding. Based on the substation vulnerability model mentioned in relevant literature, a failure probability curve for the substation can be derived, such as... Figure 3 As shown, when the waterlogging depth at the location of a substation exceeds 250mm, the probability of substation failure will increase rapidly; when the waterlogging depth exceeds 2600mm, the substation at that location can be considered to be in a state of complete failure.

[0143] 2) Ordinary nodes

[0144] Once the flood depth at the location of a distribution node exceeds the flood protection height of the equipment, the failure rate of that node will increase rapidly. Therefore, an exponential function fitting method is used to construct the relationship between the node failure rate and the flood depth. At time t, the failure rate λ of distribution node i is... i (t) is:

[0145]

[0146] Where ζ is the attenuation coefficient; exp() is the exponential function; γ is the damping coefficient; d i (t) represents the waterlogging depth at the location of node i; D Bi D is the elevation of the cable joint at node i relative to the ground. i Let i be the design flood protection height.

[0147] The failure probability of a node within [t, t+Δt] is further expressed as:

[0148]

[0149] In summary, the failure probability P of distribution node i is... i (t) simplifies to:

[0150]

[0151] Where T represents the study duration. The failure probability P mentioned in the model... i That is

[0152] S104. Construct a model for identifying weak points in the power distribution network.

[0153] Please see Figure 4Based on component characteristics, network characteristics, and fault probabilities, a three-dimensional weak zone for distribution nodes is constructed. Each distribution node can be placed in the three-dimensional graph according to its characteristic values, and the coordinate point information is represented as a triple: (component characteristic, network characteristic, fault probability). The three-dimensional partitioning shows that nodes in the weak zone are relatively important distribution equipment for the power company because they have higher component characteristics, network characteristics, and fault probabilities; conversely, nodes in the stable zone are the least important substations for the power company because their corresponding characteristic values ​​are lower.

[0154] The model allows multiple thresholds to be defined on each axis, thereby dividing the three-dimensional space into smaller regions. Figure 4 The intersection of the planes of different colors in the middle is the threshold line. The distance between the thresholds and the number of thresholds on each axis are adjustable and determined according to the priority of the relevant features and the number of samples.

[0155] Figure 4 A threshold is set for each of the three coordinate axes: 0.5 for component characteristics, 0.4 for network characteristics, and 0.5 for fault probability. The intersection of the three threshold lines (black squares in the diagram) divides the space into eight regions. The region enclosed by the three black arrows is the weak zone, and the region enclosed in the opposite direction of the arrows is the stable zone. In the weak zone, the values ​​of all three characteristics are greater than the corresponding thresholds, while in the stable zone, the values ​​of all three characteristics are less than the corresponding thresholds. Therefore, if a power company needs to reinforce distribution nodes, it should first consider nodes in the weak zone, then nodes outside the weak and stable zones, and finally nodes in the stable zone.

[0156] S2. Construct a decision optimization model for pre-disaster flood prevention and control deployment of the power distribution network;

[0157] Based on the distribution network weakness identification model, the problem of improving the flood control capability of resilient distribution networks is formulated as a two-stage stochastic optimization problem.

[0158] The two-stage stochastic optimization problem is as follows:

[0159]

[0160] Where z represents whether the distribution node is protected (a 0-1 variable); S represents the set of fault scenarios for the distribution node; y s x is a generalized variable representation of the operating state of the distribution network under disaster scenario s; i,s Let f() be the value of whether node i is flooded in scenario s (0-1 variable); f() is the distribution network loss function.

[0161] To make the problem easier to handle, the model is assumed to consider an urban power distribution network consisting of nodes (i.e., different types of substations) and lines;

[0162] Assume that distribution nodes are considered as vulnerable components of the urban power distribution network under urban flooding disasters;

[0163] Assume that the protected power distribution nodes are no longer affected by waterlogging disasters.

[0164] S201, the objective function of the model;

[0165] The fault scenarios are generated using Monte Carlo simulation technology. The specific model is divided into two stages. The first stage focuses on the protection measures of nodes before the disaster, while the second stage optimization model focuses on the load loss of the distribution network, that is, minimizing the weighted load loss under each fault scenario.

[0166] The problem model for the first stage is as follows:

[0167]

[0168] Among them, Q s () is the tracing function that provides the optimal value for the second-stage optimization problem in scenario s; For the set of distribution nodes; z i Z represents whether node i is protected (a 0-1 variable); Z is the upper limit of the number of protected nodes.

[0169] The optimization model for the second stage is as follows:

[0170]

[0171] Where, ω i Load priority weight at node i; The restored state of node i in scene s (0-1 variables); P i N The active load of node i.

[0172] The overall constraints of the two-stage stochastic optimization problem include constraints on flood control resource scheduling and system and component operation.

[0173] S202, Coupling constraints between pre-disaster resource allocation and weak points;

[0174] Given the limited resources available for flood control, these resources should be focused on weak points. Therefore, equation (13) is re-implemented through pre-disaster resource scheduling constraints and its coupling constraints with the weak point identification model.

[0175] Since weather warnings issued 12 hours before a disaster are relatively accurate, and 12 hours is sufficient for the implementation of flood prevention measures, this invention sets the implementation time for flood prevention measures to 12 hours before the disaster. To simulate the spatial uniqueness of flood prevention teams within a limited time, the following constraints are proposed:

[0176]

[0177] Where, x n,i,t Indicates whether the flood control team n is located at node i at time t (0-1 variable); τ i The time for implementing flood control measures at node i.

[0178] Because substations and other types of nodes have significantly different characteristics, the timing for implementing temporary flood protection for these two types of nodes also differs. Specific constraints are as follows (taking an inflatable barrier as an example):

[0179]

[0180] Where, d i N represents the maximum possible water depth at node i during the development of an urban flooding disaster; team The number of members in the flood control team; For the set of substation nodes; It is the set of nodes in the distribution network excluding substations.

[0181] To describe the state transition of a node (from unprotected to protected state), a 0-1 variable y is introduced. n,i,t Ensure that only one team completes the protection measures for node i and leaves after a certain period of time, and that each node undergoes at most one state transition. Specific constraints are as follows:

[0182]

[0183] Where ε is a sufficiently small positive number.

[0184] The coupling constraints between flood control resource allocation and weak points in the power distribution network are as follows:

[0185]

[0186] in, Indicates whether node i within the stable region is protected against flooding; To identify the set of nodes within the stable region of the vulnerability identification model; This indicates whether the coordinate node k outside the stable and weak regions is protected; Indicates whether node j within the vulnerable area is protected against flooding; Let v be the set of nodes excluding the stable and vulnerable zones; v is the set of nodes within the vulnerable zone. Equations (22) and (23) ensure that flood control resources are prioritized for vulnerable nodes and are not wasted on nodes within the stable zone. If there are still surplus flood control resources after all nodes within the vulnerable zone are protected, the surplus resources will be used for distribution nodes outside the vulnerable and stable zones.

[0187] S203, System and component operation constraints.

[0188] In addition to the coupling constraints of pre-disaster resource scheduling and weak points in the first stage, the second stage of the model needs to meet the system and component constraints related to the operation of the distribution network.

[0189] 1) Radial topological constraints:

[0190] In every disaster scenario, the operation of the distribution network must always satisfy the radial topology constraints, the specific constraint model of which is as follows:

[0191]

[0192]

[0193] in, These are non-negative, continuous auxiliary variables; β ij,s The connection state (0-1 variable) of line (i,j) in scenario s; A collection of distribution network lines; It is a set of distribution network nodes.

[0194] 2) Linearized power flow constraints:

[0195]

[0196] in, Let (i,j) be the active power flow of line (i,j) in scenario s (direction from i to j); Let (j,i) be the active power flow of line (j,i) in scenario s (direction from j to i); δ represents the active power output of the power source at node i in scenario s; i,s Let P be the recovery state of the load at node i in scenario s (a 0-1 variable); i N Let i be the active load of node i; Let (i,j) be the reactive power flow of line (i,j) in scenario s (direction from i to j); Let (j,i) be the reactive power flow of line (j,i) in scenario s (direction from j to i); The reactive power output of the power source at node i in scenario s; Let v be the reactive load of node i; M be a sufficiently large positive number; i,s v is the square of the voltage magnitude of node i in scene s; j,s r is the square of the voltage amplitude of node j in scene s; ij / x ij Let be the impedance of line (i,j).

[0197] 3) Line capacity constraints:

[0198] To ensure that the power flow of the line does not exceed the limit, the following capacity constraints are proposed:

[0199]

[0200] in, This represents the upper limit of the apparent power of line (i,j).

[0201] 4) Voltage amplitude constraint:

[0202] To ensure that the voltage amplitude at the nodes does not exceed the limit, the following constraints are proposed:

[0203]

[0204] in, This represents the lower limit of the voltage at node i; This represents the upper limit of the voltage at node i.

[0205] 5) Power output constraints:

[0206] To ensure that the power output does not exceed the limit, the following constraints are proposed:

[0207]

[0208] Where, η i,s Whether node i is available in scene s (0-1 variable); This represents the upper limit of the active power output of the power source at node i; A collection of distributed power sources in a power distribution network; This represents the upper limit of reactive power output at node i.

[0209] 6) Constraints related to waterlogging:

[0210] Under the disaster of rainstorm and waterlogging, once a node is submerged, its availability will change, and the switching status of connected lines will also change. Therefore, the following constraints are proposed:

[0211]

[0212] 7) Flood control constraints:

[0213] Nodes protected from flooding and those not submerged will remain usable under disaster scenarios; therefore, the state of all distribution nodes can be determined by (1-x). i,s ) and z i The logical OR operation determines the result, and the equivalent linear expression of the logical OR operation is as follows:

[0214]

[0215] Where, x i,sWhether node i is submerged in scene s (0-1 variable).

[0216] S3. Solve the decision optimization model for flood prevention and control deployment of the power distribution network before disasters.

[0217] The two-stage mixed integer stochastic optimization problem (12)-(43) was solved using an optimization solver to obtain the flood control plan for the power distribution network before disaster.

[0218] In another embodiment of the present invention, a pre-disaster flood prevention and control system for distribution networks based on resource optimization allocation is provided. This system can be used to implement the above-mentioned pre-disaster flood prevention and control method for distribution networks based on resource optimization allocation. Specifically, the pre-disaster flood prevention and control system for distribution networks based on resource optimization allocation includes a partitioning module, a construction module, and a control module.

[0219] Among them, the partitioning module uses the component characteristics, network characteristics and fault probability of power distribution network equipment as labels, and realizes the partitioning of equipment weakness by constructing a three-dimensional spatial map.

[0220] The module is constructed to couple the dispatch of flood control personnel with the obtained weak point zoning, and to build a decision optimization model for pre-disaster flood control deployment of the power distribution network;

[0221] The deployment module uses an optimization solver to solve the decision optimization model of the pre-disaster flood prevention deployment of the power distribution network, and obtains the pre-disaster flood prevention deployment scheme of the power distribution network.

[0222] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes 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 (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used in the operation of a pre-disaster flood prevention deployment method for power distribution networks based on resource optimization allocation, including:

[0223] Using the component characteristics, network characteristics, and fault probabilities of power distribution network equipment as labels, a three-dimensional spatial map is constructed to partition the equipment based on its vulnerability level. The scheduling of flood control personnel is coupled with the obtained vulnerability partitioning to construct a decision optimization model for pre-disaster flood control deployment of the power distribution network. The optimization solver is used to solve the obtained decision optimization model for pre-disaster flood control deployment of the power distribution network to obtain a pre-disaster flood control deployment scheme for the power distribution network.

[0224] Please see Figure 7 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fluid composition calculation method in the reservoir stimulation wellbore of this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the pre-disaster flood prevention and control system for the power distribution network based on resource optimization allocation of this embodiment. To avoid repetition, these details are not elaborated here.

[0225] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 7 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0226] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), 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.

[0227] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0228] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0229] Please see Figure 8 The terminal device 600 is an electronic device, which takes the form of a general-purpose 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 storage unit 620 and processing unit 610), a display unit 640, etc.

[0230] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 9 The steps are shown in the figure.

[0231] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0232] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0233] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0234] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with 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.

[0235] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor; these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0236] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the pre-disaster flood prevention and control method for power distribution networks based on resource optimization allocation in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0237] Using the component characteristics, network characteristics, and fault probabilities of power distribution network equipment as labels, a three-dimensional spatial map is constructed to partition the equipment based on its vulnerability level. The scheduling of flood control personnel is coupled with the obtained vulnerability partitioning to construct a decision optimization model for pre-disaster flood control deployment of the power distribution network. The optimization solver is used to solve the obtained decision optimization model for pre-disaster flood control deployment of the power distribution network to obtain a pre-disaster flood control deployment scheme for the power distribution network.

[0238] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0239] Please see Figure 5 An improved IEEE 33-node distribution network, coupled with a transportation network, was used as a test system for a case study to verify the effectiveness of the proposed method. All calculations for the distribution network flood control decision model were performed using a personal computer equipped with an Intel Core i5-13500H processor and 16GB of RAM. The specific construction and solution of the model were implemented using Python 3.10.9 and Gurobi 10.0.1.

[0240] The attenuation coefficient of ordinary nodes is set to 0.02; the damping coefficient γ = 4; the elevation of the cable joint to the ground is 200mm; and the design flood protection height is 400mm.

[0241] The final failure probability of the nodes in the test system is as follows: Figure 6 As shown, the color intensity of the bubbles corresponds to the probability of failure, with the top three nodes having the highest failure probabilities being 20, 4, and 17. Table 1 displays relevant information for the six nodes with the highest failure probabilities.

[0242] Table 1. Node information with high failure probability

[0243]

[0244] The weighting coefficients for the features are set as follows: a = 0.4, b = 0.2, c = 0.4; u = 0.5, v = 0.5; and the cost price of the substation node is specified. The cost is 40 million RMB; the cost price of a regular node. The repair time is calculated when the cost is 100,000 RMB and the probability of failure is 1. The time limit is 90 minutes. The weight coefficient of all nodes is uniformly set to 1; the network characteristic of the substation nodes is set to the highest value among the nodes.

[0245] The feature thresholds in the vulnerability identification model are set according to the priority of the features and the number of nodes: component features and failure probabilities have higher priority, so 20% of the samples are divided below the threshold based on the feature values; the threshold for network features is set to the median of the samples; the final node vulnerability partitioning is shown in Table 2.

[0246] Table 2 Weak Zone Results for Distribution Nodes

[0247]

[0248] In the resource scheduling model, the flood prevention time is set to 12 hours before the disaster; the number of members in the flood prevention team is set to 4. This model is coupled with the weak zone model to the decision optimization model for pre-disaster flood prevention deployment in the distribution network. The final flood prevention scheme obtained by solving the optimization model is to take temporary flood protection measures for seven distribution nodes. To verify the effectiveness of the model, the empirical flood prevention decision scheme (selecting the seven nodes with the most severe flooding for protection) is compared with the solution results of the proposed stochastic optimization decision model, as shown in Table 3.

[0249] Table 3 Comparison of results between the two schemes

[0250]

[0251] Compared with empirical flood prevention methods, the method of this invention can significantly reduce the risk of power outages.

[0252] In summary, this invention presents a pre-disaster flood prevention deployment method and system for power distribution networks based on resource optimization allocation. It considers equipment failure probabilities, component characteristics, and network characteristics, and proposes a power distribution network weak point identification model and a two-stage stochastic optimization decision-making model for power distribution network flood prevention. As a preventative measure, the output scheme of the flood prevention decision model can significantly reduce the risk of power outages in urban power distribution networks during rainstorm flooding disasters, thereby reducing social and economic losses.

[0253] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0254] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0255] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0256] In the embodiments provided by this 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 merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0257] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0258] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0259] If the integrated module / unit is implemented as 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed 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 electrical carrier signals and telecommunication signals.

[0260] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0261] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0262] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0263] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for pre-disaster flood prevention deployment in power distribution networks based on resource optimization allocation, characterized in that, Includes the following steps: Using the component characteristics, network characteristics, and fault probabilities of distribution network equipment as labels, a three-dimensional spatial map is constructed to partition the equipment based on its vulnerability level. The partitioning is specifically as follows: Model the component characteristics of distribution network equipment, including load, expected repair time, and expected damage cost. The component characteristics CF of distribution node i are also considered. i Expressed as: Where a, b, and c represent the weighting coefficients of various component characteristics, a + b + c = 100%; ω i P represents the weight coefficient of node i; i N The load at node i; DC i Let be the expected damage cost of node i; Let i be the expected repair time for node i; The network characteristics of distribution network equipment are modeled, including the degree centrality and betweenness centrality of nodes, and the network characteristics NF of distribution node i. i Expressed as: Where u and v are the weight coefficients of various network features; dc i Let bc represent the degree centrality of node i. i Let i be the betweenness centrality of node i; The solution is divided into two categories based on whether the node is a substation, to obtain the failure probability P of the distribution network equipment under waterlogging disaster. i (t) is: Where T is the study duration, λ i (t) represents the failure rate of distribution node i, and Δt represents the time step. A weak point identification model for distribution network is constructed based on the component characteristics, network characteristics, and fault probability of distribution network equipment. Each distribution node is placed in a three-dimensional graph according to its characteristic value, and the coordinate point information represents the component characteristics, network characteristics, and fault probability. By coupling the dispatch of flood control personnel with the obtained weak point zoning, a decision optimization model for pre-disaster flood control deployment of the power distribution network is constructed. The specific decision optimization model for pre-disaster flood control deployment of the power distribution network is as follows: Where z represents whether the distribution node is protected (a 0-1 variable); S represents the set of fault scenarios for the distribution node; y s x is a generalized variable representation of the operating state of the distribution network under disaster scenario s; i,s Let f(·) indicate whether node i is flooded in scenario s; f(·) is the distribution network loss function. The decision optimization model for pre-disaster flood prevention and control of the power distribution network is solved using an optimization solver to obtain the pre-disaster flood prevention and control scheme for the power distribution network.

2. The method for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation according to claim 1, characterized in that, The decision optimization model for pre-disaster flood prevention and control in power distribution networks is divided into two stages. The first stage focuses on the protection measures for pre-disaster nodes, with constraints including flood prevention resource scheduling constraints and system and component operation constraints. The model for the first stage is as follows: Among them, Q s (·) is the tracing function that provides the optimal value for the second-stage optimization problem in scenario s; For the set of distribution nodes; z i Z represents whether node i is protected (a 0-1 variable); Z is the upper limit of the number of protected nodes. The second-stage optimization model focuses on the load shedding of the distribution network. Where, ω i The load priority weight at node i; The restored state of node i in scene s; The active load of node i.

3. The method for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation according to claim 2, characterized in that, Constraints on flood control resource allocation: The implementation time for flood control measures is set at 12 hours before the disaster, simulating the spatial uniqueness of flood control teams within a limited time, with the following constraints: Where, x n,i,t Indicates whether the flood control team n is located at node i at time t; τ i The timeframe for implementing flood control measures at node i; The time constraints for implementing temporary flood protection at substations and other types of nodes are as follows: Where, d i N represents the maximum possible water depth at node i during the development of an urban flooding disaster; team The number of members in the flood control team; For the set of substation nodes; It is the set of nodes in the distribution network excluding substations; To describe the state transitions of nodes, a 0-1 variable y is introduced. n,i,t Ensure that only one team completes the protection measures for node i and leaves after a certain period of time, and that each node undergoes at most one state transition; specific constraints are as follows: Where ε is a sufficiently small positive number; x n,i,t Indicates whether the flood control team n is located at node i at time t; y n,i,t Indicates whether node i was repaired by flood control team n at time t; The coupling constraints between flood control resource allocation and weak points in the power distribution network are as follows: in, Indicates whether node i within the stable region is protected against flooding; To identify the set of nodes within the stable region of the vulnerability identification model; This indicates whether the coordinate node k outside the stable and weak regions is protected; Indicates whether node j within the vulnerable area is protected against flooding; This is the set of nodes excluding the stable and weak regions. This refers to the set of nodes within the weak zone.

4. The method for pre-disaster flood prevention and control of power distribution networks based on resource optimization allocation according to claim 2, characterized in that, System and component operating constraints: Radial topological constraints: in, β is a non-negative, continuous auxiliary variable. ij,s The connection state (0-1 variable) of line (i,j) in scenario s; A collection of distribution network lines; For the set of distribution network nodes; Linearized power flow constraints: in, Let (i,j) be the active power flow of line (i,j) in scenario s; Let (j,i) be the active power flow of line (j,i) in scenario s; δ represents the active power output of the power source at node i in scenario s; i,s P represents the recovery state of the load at node i in scenario s; i N Let i be the active load of node i; Let (i,j) be the reactive power flow of line (i,j) in scenario s; Let (j,i) be the reactive power flow of line (j,i) in scenario s; The reactive power output of the power source at node i in scenario s; Let v be the reactive load of node i; M be a sufficiently large positive number; i,s v is the square of the voltage magnitude of node i in scene s; j,s r is the square of the voltage amplitude of node j in scene s; ij / x ij Let (i,j) be the impedance of the line. Line capacity constraints: in, This represents the upper limit of the apparent power of line (i,j); Voltage amplitude constraint: in, This represents the lower limit of the voltage at node i; This represents the upper limit of the voltage at node i; Power output constraints: Where, η i,s Whether node i is available in scenario s; This represents the upper limit of the active power output of the power source at node i; A collection of distributed power sources in a power distribution network; This represents the upper limit of the reactive power output of the power supply at node i. Constraints on the impact of waterlogging: Flood control constraints: Where, x i,s Whether node i is submerged in scene s; δ i,s The recovery state of the load at node i in scenario s; η i,s Whether node i is available in scene s; β ij,s Let z be the connection state (0-1 variable) of line (i,j) in scenario s; i Whether node i is protected against flooding.

5. A pre-disaster flood prevention and control system for power distribution networks based on resource optimization allocation, characterized in that, include: The partitioning module uses the component characteristics, network characteristics, and fault probabilities of distribution network equipment as labels. It constructs a three-dimensional spatial map to partition the equipment based on its vulnerability level. The partitioning is as follows: Model the component characteristics of distribution network equipment, including load, expected repair time, and expected damage cost. The component characteristics CF of distribution node i are also considered. i Expressed as: Where a, b, and c represent the weighting coefficients of various component characteristics, a + b + c = 100%; ω i P represents the weight coefficient of node i; i N The load at node i; DC i Let be the expected damage cost of node i; Let i be the expected repair time for node i; The network characteristics of distribution network equipment are modeled, including the degree centrality and betweenness centrality of nodes, and the network characteristics NF of distribution node i. i Represented as: Where u and v are the weight coefficients of various network features; dc i Let bc represent the degree centrality of node i. i Let i be the betweenness centrality of node i; The solution is divided into two categories based on whether the node is a substation, to obtain the failure probability P of the distribution network equipment under waterlogging disaster. i (t) is: Where T is the study duration, λ i (t) represents the failure rate of distribution node i, and Δt represents the time step. A weak point identification model for distribution network is constructed based on the component characteristics, network characteristics, and fault probability of distribution network equipment. Each distribution node is placed in a three-dimensional graph according to its characteristic value, and the coordinate point information represents the component characteristics, network characteristics, and fault probability. The module couples the dispatch of flood control personnel with the obtained weak point zoning to construct a decision optimization model for pre-disaster flood control deployment of the power distribution network. The specific decision optimization model for pre-disaster flood control deployment of the power distribution network is as follows: Where z represents whether the distribution node is protected (a 0-1 variable); S represents the set of fault scenarios for the distribution node; y s x is a generalized variable representation of the operating state of the distribution network under disaster scenario s; i,s Let f(·) indicate whether node i is flooded in scenario s; f(·) is the distribution network loss function. The deployment module uses an optimization solver to solve the decision optimization model of the pre-disaster flood prevention deployment of the power distribution network, and obtains the pre-disaster flood prevention deployment scheme of the power distribution network.

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