Power distribution system recovery method and device under rainstorm and waterlogging disasters

Abstract

The invention relates to the field of power distribution network fault handling and reliability research, and particularly provides a power distribution system recovery method and device under rainstorm and waterlogging disasters, and the method comprises the steps: generating a typical scene during the occurrence of the rainstorm and waterlogging disasters through employing a Monte Carlo method based on a power distribution network equipment failure probability function during the occurrence of the rainstorm and waterlogging disasters; solving a power distribution network reconstruction optimization model in a typical scene during the occurrence period of the rainstorm and waterlogging disaster to obtain a switch optimization result during the occurrence period of the rainstorm and waterlogging disaster; performing power distribution network reconstruction by using the switch optimization result, and after the rainstorm waterlogging disaster is finished, solving a power distribution network fault recovery model to obtain a mobile emergency energy storage equipment scheduling result; recovering the power distribution network after the rainstorm waterlogging disaster by using the scheduling result of the mobile emergency energy storage equipment; according to the scheme, the recovery capability of the system is remarkably improved, and unique advantages are particularly shown in complex scenes with serious traffic disasters.

Description

Technical Field

[0001] The present invention relates to the field of distribution network fault handling and reliability research, and in particular to a method and device for restoring a distribution system under a rainstorm and waterlogging disaster. Background Art

[0002] When extreme weather disasters such as heavy rain occur, power infrastructure faces serious safety risks, which makes the resilience of the power system an important requirement for its development. Resilience refers to the ability of the power system to respond and recover when encountering disasters or damage, covering preventive measures before the disaster, protection capabilities during the disaster, and rapid recovery after the disaster. A power system with good resilience can effectively predict and adapt to extreme events, respond quickly and recover from disasters, and minimize load losses. By introducing a variety of distributed energy sources and intelligent switching equipment, the new distribution network can flexibly adjust the flow of electricity, and ensure continuous power supply to users through system reconstruction or islanding operations when extreme events occur.

[0003] Heavy rain and floods are different from other extreme weather events. Their destructiveness is not only reflected in the physical damage to the distribution network, but also involves the paralysis of more complex urban infrastructure. In the waterlogging caused by heavy rain, the distribution network may cause power supply interruptions due to line breakage, tower collapse, etc., which directly affects the electricity demand of the majority of users. The intensification of urban waterlogging will cause more serious damage to the equipment of the distribution network. For example, electrical equipment such as substations and distribution boxes may be flooded, causing them to completely fail. In addition, severe waterlogging during heavy rains will also cause serious congestion in the transportation system, making it difficult for personnel and equipment for emergency repairs and recovery work to arrive at the site smoothly, thereby delaying the process of post-disaster recovery. In the process of post-disaster recovery, the power system needs to rely on a large number of emergency resources, such as mobile energy storage equipment and portable generators. However, the waterlogging and traffic obstruction of heavy rain and floods will make the dispatch of these key resources more complicated, and it is impossible to deliver the necessary equipment and technicians to the affected areas in time. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes a method and device for restoring a power distribution system under a rainstorm and waterlogging disaster.

[0005] In a first aspect, a method for restoring a power distribution system under a rainstorm and waterlogging disaster is provided, wherein the method for restoring a power distribution system under a rainstorm and waterlogging disaster comprises:

[0006] Based on the probability function of distribution network equipment failure during heavy rain and waterlogging disasters, the Monte Carlo method is used to generate typical scenarios during heavy rain and waterlogging disasters.

[0007] Under the typical scenario during the occurrence of the torrential rain and waterlogging disaster, the distribution network reconstruction optimization model is solved to obtain the switch optimization results during the occurrence of the torrential rain and waterlogging disaster;

[0008] The switch optimization results are used to reconstruct the distribution network, and after the rainstorm and waterlogging disaster ends, the distribution network fault recovery model is solved to obtain the dispatch result of the mobile emergency energy storage device;

[0009] Using the dispatching results of the mobile emergency energy storage equipment to restore the distribution network after the rainstorm and waterlogging disaster;

[0010] The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

[0011] Preferably, the failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows:

[0012]

[0013] In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

[0014] Furthermore, the process of obtaining the water depth in the area where the equipment is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth over time in the area where each equipment is located under heavy rain conditions.

[0015] Preferably, the distribution network reconstruction optimization model includes: a first objective function with the goal of minimizing the amount of load loss, component constraints, topology constraints and power flow constraints.

[0016] Furthermore, the first objective function is as follows:

[0017]

[0018] In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switchis the cost of a single switch operation, N line is a collection of lines, is the switch state on line u at time t, is the switch state of line u at time t-1.

[0019] Furthermore, the element constraints are as follows:

[0020]

[0021]

[0022] In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the time interval length, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

[0023] Furthermore, the topological constraints are as follows:

[0024]

[0025] In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

[0026] Furthermore, the power flow constraints are as follows:

[0027]

[0028] In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

[0029] Preferably, the distribution network fault recovery model includes: a second objective function with the goal of minimizing the mobile resource scheduling cost, a traffic network constraint, a component constraint, a topology constraint and a power flow constraint.

[0030] Furthermore, the second objective function is as follows:

[0031]

[0032] In the above formula, A is the total cost, c load is the unit loss load cost, N bus is the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

[0033] Furthermore, the traffic network constraints are as follows:

[0034]

[0035] In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

[0036] In a second aspect, a power distribution system recovery device under a rainstorm and waterlogging disaster is provided, wherein the power distribution system recovery device under a rainstorm and waterlogging disaster comprises:

[0037] The simulation module is used to generate typical scenarios during the period of heavy rain and waterlogging disasters using the Monte Carlo method based on the probability function of distribution network equipment failure during the period of heavy rain and waterlogging disasters;

[0038] The first analysis module is used to solve the distribution network reconstruction optimization model in a typical scenario during the occurrence of the rainstorm and waterlogging disaster, and obtain the switch optimization result during the occurrence of the rainstorm and waterlogging disaster;

[0039] The second analysis module is used to use the switch optimization results to reconstruct the distribution network, solve the distribution network fault recovery model after the rainstorm and waterlogging disaster ends, and obtain the dispatch result of the mobile emergency energy storage device;

[0040] A recovery module, used to use the dispatch results of the mobile emergency energy storage device to restore the distribution network after the rainstorm and waterlogging disaster;

[0041] The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

[0042] Preferably, the failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows:

[0043]

[0044] In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

[0045] Furthermore, the process of obtaining the water depth in the area where the equipment is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth over time in the area where each equipment is located under heavy rain conditions.

[0046] Preferably, the distribution network reconstruction optimization model includes: a first objective function with the goal of minimizing the amount of load loss, component constraints, topology constraints and power flow constraints.

[0047] Furthermore, the first objective function is as follows:

[0048]

[0049] In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switch is the cost of a single switch operation, N line is a collection of lines, is the switch state on line u at time t, is the switch state of line u at time t-1.

[0050] Furthermore, the element constraints are as follows:

[0051]

[0052] In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the time interval length, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

[0053] Furthermore, the topological constraints are as follows:

[0054]

[0055]

[0056] In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

[0057] Furthermore, the power flow constraints are as follows:

[0058]

[0059] In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

[0060] Preferably, the distribution network fault recovery model includes: a second objective function with the goal of minimizing the mobile resource scheduling cost, a traffic network constraint, a component constraint, a topology constraint and a power flow constraint.

[0061] Furthermore, the second objective function is as follows:

[0062]

[0063] In the above formula, A is the total cost, c load is the unit loss load cost, N bus is the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

[0064] Furthermore, the traffic network constraints are as follows:

[0065]

[0066] In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

[0067] In a third aspect, a computer device is provided, comprising: one or more processors;

[0068] The processor is configured to execute one or more programs;

[0069] When the one or more programs are executed by the one or more processors, the power distribution system recovery method under heavy rain and waterlogging disasters is implemented.

[0070] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the method for restoring the power distribution system under the rainstorm and waterlogging disaster is implemented.

[0071] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0072] The present invention provides a method and device for restoring a power distribution system under a rainstorm and waterlogging disaster, comprising: based on a probability function of failure of distribution network equipment during a rainstorm and waterlogging disaster, using a Monte Carlo method to generate a typical scenario during a rainstorm and waterlogging disaster; solving a distribution network reconstruction optimization model in the typical scenario during the rainstorm and waterlogging disaster to obtain a switch optimization result during the rainstorm and waterlogging disaster; reconstructing the distribution network using the switch optimization result, and solving a distribution network fault recovery model after the rainstorm and waterlogging disaster to obtain a mobile emergency energy storage device scheduling result; restoring the distribution network after a rainstorm and waterlogging disaster using the mobile emergency energy storage device scheduling result; the solution significantly improves the recovery capability of the system, and especially shows unique advantages in complex scenarios where traffic is severely affected, specifically:

[0073] The technical solution provided by the present invention can accurately analyze the impact of heavy rain and water accumulation on power equipment and quickly locate key fault points through dynamic modeling of the distribution network and transportation network. During the disaster, the distribution network is reconstructed using intelligent switching technology to divide the isolated power supply area, effectively reduce load loss, achieve rapid recovery of some areas, and shorten the power supply interruption time of the entire system. In the post-disaster stage, through a multi-period fault recovery model, the damage to the distribution network and transportation network is comprehensively considered, and emergency resources such as mobile energy storage equipment and portable generators are reasonably dispatched to ensure that emergency equipment can quickly reach the disaster area, thereby significantly improving the response efficiency of post-disaster recovery and gaining precious recovery time. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a flow chart of main steps of a method for restoring a power distribution system under a rainstorm waterlogging disaster according to an embodiment of the present invention;

[0075] Figure 2 This is a structural diagram of an IEEE33-node distribution network according to an embodiment of the present invention;

[0076] Figure 3 is a diagram of a Sioux Falls 24-node transportation network structure according to an embodiment of the present invention;

[0077] Figure 4 is a reconstruction result diagram of time period t in an embodiment of the present invention;

[0078] Figure 5 is a reconstruction result diagram of the t+1 period of the embodiment of the present invention;

[0079] Figure 6 is a reconstruction result diagram of the time period t+2 of an embodiment of the present invention;

[0080] Figure 7 is a reconstruction result diagram of the t+3 period of the embodiment of the present invention;

[0081] Figure 8 This is a dispatch result diagram of an embodiment of the present invention taking a mobile energy storage vehicle as an example. DETAILED DESCRIPTION

[0082] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0083] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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, 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.

[0084] As disclosed in the background technology, when extreme weather disasters such as heavy rain occur, the power infrastructure faces serious safety risks, which makes the resilience of the power system an important requirement for its development. Resilience refers to the ability of the power system to respond and recover when encountering disasters or damage, covering preventive measures before the disaster, protection capabilities during the disaster, and rapid recovery after the disaster. A power system with good resilience can effectively predict and adapt to extreme events, respond quickly and recover from disasters, and minimize load losses. By introducing a variety of distributed energy sources and intelligent switching equipment, the new distribution network can flexibly adjust the flow of electricity, and ensure continuous power supply to users through system reconstruction or islanding operations when extreme events occur.

[0085] Heavy rain and floods are different from other extreme weather events. Their destructiveness is not only reflected in the physical damage to the distribution network, but also involves the paralysis of more complex urban infrastructure. In the waterlogging caused by heavy rain, the distribution network may cause power supply interruptions due to line breakage, tower collapse, etc., which directly affects the electricity demand of the majority of users. The intensification of urban waterlogging will cause more serious damage to the equipment of the distribution network. For example, electrical equipment such as substations and distribution boxes may be flooded, causing them to completely fail. In addition, severe waterlogging during heavy rains will also cause serious congestion in the transportation system, making it difficult for personnel and equipment for emergency repairs and recovery work to arrive at the site smoothly, thereby delaying the process of post-disaster recovery. In the process of post-disaster recovery, the power system needs to rely on a large number of emergency resources, such as mobile energy storage equipment and portable generators. However, the waterlogging and traffic obstruction of heavy rain and floods will make the dispatch of these key resources more complicated, and it is impossible to deliver the necessary equipment and technicians to the affected areas in time.

[0086] In order to improve the above problems, the present invention provides a method and device for restoring a distribution system under a rainstorm and waterlogging disaster, comprising: based on the failure probability function of distribution network equipment during the rainstorm and waterlogging disaster, using the Monte Carlo method to generate a typical scenario during the rainstorm and waterlogging disaster; in the typical scenario during the rainstorm and waterlogging disaster, solving the distribution network reconstruction optimization model to obtain the switch optimization result during the rainstorm and waterlogging disaster; using the switch optimization result to reconstruct the distribution network, and after the rainstorm and waterlogging disaster is over, solving the distribution network fault recovery model to obtain the mobile emergency energy storage device scheduling result; using the mobile emergency energy storage device scheduling result to restore the distribution network after the rainstorm and waterlogging disaster; this solution significantly improves the recovery capability of the system, especially showing unique advantages in complex scenarios where traffic is severely affected, specifically:

[0087] The technical solution provided by the present invention can accurately analyze the impact of heavy rain and water accumulation on power equipment and quickly locate key fault points through dynamic modeling of the distribution network and transportation network. During the disaster, the distribution network is reconstructed using intelligent switching technology to divide the isolated power supply area, effectively reduce load loss, achieve rapid recovery of some areas, and shorten the power supply interruption time of the entire system. In the post-disaster stage, through a multi-period fault recovery model, the damage to the distribution network and transportation network is comprehensively considered, and emergency resources such as mobile energy storage equipment and portable generators are reasonably dispatched to ensure that emergency equipment can quickly reach the disaster area, thereby significantly improving the response efficiency of post-disaster recovery and gaining precious recovery time.

[0088] The above scheme is described in detail below.

[0089] Example 1

[0090] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for restoring a power distribution system under a rainstorm and waterlogging disaster according to an embodiment of the present invention. Figure 1 As shown, the power distribution system recovery method under rainstorm waterlogging disaster in the embodiment of the present invention mainly includes the following steps:

[0091] Step S101: Based on the probability function of distribution network equipment failure during a rainstorm and waterlogging disaster, a typical scenario during a rainstorm and waterlogging disaster is generated using a Monte Carlo method;

[0092] Step S102: Under the typical scenario of the rainstorm and waterlogging disaster, solving the distribution network reconstruction optimization model to obtain the switch optimization result during the rainstorm and waterlogging disaster;

[0093] Step S103: Reconstruct the distribution network using the switch optimization result, and after the rainstorm and waterlogging disaster ends, solve the distribution network fault recovery model to obtain the dispatch result of the mobile emergency energy storage device;

[0094] Step S104: using the dispatch result of the mobile emergency energy storage device to restore the distribution network after the rainstorm and waterlogging disaster;

[0095] The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

[0096] In this embodiment, the failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows:

[0097]

[0098] In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

[0099] In one embodiment, the process of obtaining the water depth in the area where the device is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth over time in the area where each device is located under heavy rain conditions.

[0100] In a specific embodiment, the rainfall intensity formula of the Chicago Rain Type Method is:

[0101]

[0102] Among them, i is the instantaneous rainfall intensity at time t, M is the rain force parameter, n is the rainfall duration correction parameter, Q is the rain force change parameter, and d is the rainfall intensity parameter. n is the rainfall attenuation index, and P is the rainstorm return period.

[0103] Based on the rainfall data, a two-dimensional surface hydrodynamic model is used to simulate the water flow process, taking into account the balance of rainfall, drainage capacity and water flow to obtain the change of water depth in each area. The formula of the two-dimensional surface hydrodynamic model is:

[0104]

[0105] Among them, ρ is the density of water, h is the depth of water accumulation, g is the acceleration of gravity, d is the water level, V is the water flow velocity, l is the width of the grid, a is the unit conversion coefficient, k is the Manning roughness coefficient, i is the instantaneous rainfall intensity, m is the mass, and Δt is the time interval.

[0106] Model of damage to distribution network equipment caused by water depth: Based on the simulation results of water depth, a damage model of water depth to distribution network equipment (such as substations, distribution boxes, switchgear, etc.) is established. This model needs to consider the water resistance of different equipment and the degree of failure or damage that water depth may cause to the equipment, so as to evaluate the failure probability and damage degree of each equipment in the case of heavy rain and floods.

[0107] Specifically, a damage model of water accumulation depth on distribution network equipment is established: based on the simulation results of water accumulation depth, the possibility of failure of distribution network equipment (such as substations, distribution boxes, switchgear, etc.) caused by water accumulation of different depths is analyzed. This model mainly considers the impact of water resistance of different equipment, water accumulation depth and its duration on equipment reliability. Through this model, the failure probability and damage degree of each equipment in the case of heavy rain and flooding can be quantified, so as to carry out emergency response more accurately.

[0108] Specifically, in the failure model, when the water depth is set to exceed a certain critical value, the failure probability of the equipment will increase significantly.

[0109] In this embodiment, intelligent switches are used to automatically detect and isolate fault areas, adjust the flow of power, divide island power supply areas, and simultaneously divide islands and reconstruct, thereby maximizing the stable operation of the distribution network. Specifically, the distribution network reconstruction optimization model includes: a first objective function with the minimum load loss as the goal, component constraints, topology constraints, and flow constraints.

[0110] In one embodiment, the first objective function is as follows:

[0111]

[0112] In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switch is the cost of a single switch operation, N line is a collection of lines, is the switch state on line u at time t, is the switch state of line u at time t-1.

[0113] In one embodiment, the element constraints are as follows:

[0114]

[0115] In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the time interval length, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

[0116] In one embodiment, the topology constraints are as follows:

[0117]

[0118]

[0119] In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

[0120] In one embodiment, the power flow constraints are as follows:

[0121]

[0122] In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

[0123] In this embodiment, during the post-disaster recovery process, the repair of the distribution network and the restoration of power need to rely on a large number of mobile emergency resources. Since heavy rains and floods usually cause damage to the transportation network, the scheduling of mobile resources (such as mobile energy storage equipment, portable generators, etc.) will face great challenges. Therefore, the third part of the present invention focuses on taking into account the impact of water accumulation, establishing a dynamic fault recovery model, comprehensively considering the damage to the transportation network and the distribution network, and rationally scheduling emergency resources such as mobile energy storage to maximize the power supply restoration load and improve the recovery efficiency of the power system. Mainly consider the constraints of the transportation network and the operating limitations of the comprehensive distribution network. By solving the model through commercial optimization software, the optimal recovery strategy can be obtained. Specifically: the distribution network fault recovery model includes: a second objective function with the minimum mobile resource scheduling cost as the goal, transportation network constraints, component constraints, topology constraints and flow constraints.

[0124] In one embodiment, the second objective function is as follows:

[0125]

[0126] In the above formula, A is the total cost, c load is the unit loss load cost, N bus is the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

[0127] In one embodiment, the traffic network constraints are as follows:

[0128]

[0129] In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

[0130] In an application scenario, Figure 2 and Figure 3 The simulation is carried out in an urban network consisting of IEEE 33-node distribution network and Sioux Falls 24-node transportation network. DG is a distributed power source. A total of 6 mobile energy storage vehicles are deployed. The 50-year return period rainfall is used as the simulation. Figure 4 , 5The reconstruction results of t, t+1, t+2, and t+3 of 6 and 7 are obtained through step S103. Figure 8 Taking a mobile energy storage vehicle as an example, the dispatching results effectively support the post-disaster distribution network.

[0131] Example 2

[0132] Based on the same inventive concept, the present invention also provides a power distribution system recovery device under a rainstorm waterlogging disaster, the power distribution system recovery device under a rainstorm waterlogging disaster comprises:

[0133] The simulation module is used to generate typical scenarios during the period of heavy rain and waterlogging disasters using the Monte Carlo method based on the probability function of distribution network equipment failure during the period of heavy rain and waterlogging disasters;

[0134] The first analysis module is used to solve the distribution network reconstruction optimization model in a typical scenario during the occurrence of the rainstorm and waterlogging disaster, and obtain the switch optimization result during the occurrence of the rainstorm and waterlogging disaster;

[0135] The second analysis module is used to use the switch optimization results to reconstruct the distribution network, solve the distribution network fault recovery model after the rainstorm and waterlogging disaster ends, and obtain the dispatch result of the mobile emergency energy storage device;

[0136] A recovery module, used to use the dispatch results of the mobile emergency energy storage device to restore the distribution network after the rainstorm and waterlogging disaster;

[0137] The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

[0138] Preferably, the failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows:

[0139]

[0140] In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

[0141] Furthermore, the process of obtaining the water depth in the area where the equipment is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth over time in the area where each equipment is located under heavy rain conditions.

[0142] Preferably, the distribution network reconstruction optimization model includes: a first objective function with the goal of minimizing the amount of load loss, component constraints, topology constraints and power flow constraints.

[0143] Furthermore, the first objective function is as follows:

[0144]

[0145] In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switch is the cost of a single switch operation, N line is a collection of lines, is the switch state on line u at time t, is the switch state of line u at time t-1.

[0146] Furthermore, the element constraints are as follows:

[0147]

[0148]

[0149] In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the time interval length, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

[0150] Furthermore, the topological constraints are as follows:

[0151]

[0152] In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

[0153] Furthermore, the power flow constraints are as follows:

[0154]

[0155] In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

[0156] Preferably, the distribution network fault recovery model includes: a second objective function with the goal of minimizing the mobile resource scheduling cost, a traffic network constraint, a component constraint, a topology constraint and a power flow constraint.

[0157] Furthermore, the second objective function is as follows:

[0158]

[0159] In the above formula, A is the total cost, c load is the unit loss load cost, N busis the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

[0160] Furthermore, the traffic network constraints are as follows:

[0161]

[0162]

[0163] In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

[0164] Example 3

[0165] Based on the same inventive concept, the present invention also provides a computer device, 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 in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a distribution system recovery method under a rainstorm waterlogging disaster in the above embodiment.

[0166] Example 4

[0167] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer 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 (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a distribution system recovery method under a rainstorm waterlogging disaster in the above embodiment.

[0168] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of 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 produce 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.

[0170] 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 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] 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.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for restoring a power distribution system under a rainstorm waterlogging disaster, characterized in that: The method comprises: Based on the probability function of distribution network equipment failure during heavy rain and waterlogging disasters, the Monte Carlo method is used to generate typical scenarios during heavy rain and waterlogging disasters. Under the typical scenario during the occurrence of the torrential rain and waterlogging disaster, the distribution network reconstruction optimization model is solved to obtain the switch optimization results during the occurrence of the torrential rain and waterlogging disaster; The switch optimization results are used to reconstruct the distribution network, and after the rainstorm and waterlogging disaster ends, the distribution network fault recovery model is solved to obtain the dispatch result of the mobile emergency energy storage device; Using the dispatching results of the mobile emergency energy storage equipment to restore the distribution network after the rainstorm and waterlogging disaster; The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

2. The method according to claim 1, characterized in that The failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows: In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

3. The method according to claim 2, characterized in that The process of obtaining the water depth in the area where the equipment is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth in the area where each equipment is located over time under heavy rain conditions.

4. The method according to claim 1, characterized in that The distribution network reconstruction optimization model includes: a first objective function with the goal of minimizing the amount of load loss, component constraints, topology constraints and power flow constraints.

5. The method according to claim 4, characterized in that The first objective function is as follows: In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switch is the cost of a single switch operation, N line is a collection of lines, is the switch state on line u at time t, is the switch state of line u at time t-1.

6. The method according to claim 4, characterized in that The element constraints are as follows: In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the length of the time interval, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

7. The method according to claim 4, characterized in that The topological constraints are as follows: In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation set, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

8. The method according to claim 4, characterized in that The power flow constraints are as follows: In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

9. The method according to claim 1, characterized in that The distribution network fault recovery model includes: a second objective function with the goal of minimizing the mobile resource scheduling cost, a traffic network constraint, a component constraint, a topology constraint and a power flow constraint.

10. The method according to claim 9, characterized in that The second objective function is as follows: In the above formula, A is the total cost, c load is the unit loss load cost, N bus is the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

11. The method according to claim 10, characterized in that The traffic network constraints are as follows: In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

12. A power distribution system recovery device under rainstorm waterlogging disaster, characterized in that: The device comprises: The simulation module is used to generate typical scenarios during the period of heavy rain and waterlogging disasters using the Monte Carlo method based on the probability function of distribution network equipment failure during the period of heavy rain and waterlogging disasters; The first analysis module is used to solve the distribution network reconstruction optimization model in a typical scenario during the occurrence of the rainstorm and waterlogging disaster, and obtain the switch optimization result during the occurrence of the rainstorm and waterlogging disaster; The second analysis module is used to use the switch optimization results to reconstruct the distribution network, solve the distribution network fault recovery model after the rainstorm and waterlogging disaster ends, and obtain the dispatch result of the mobile emergency energy storage device; A recovery module, used to use the dispatch results of the mobile emergency energy storage device to restore the distribution network after the rainstorm and waterlogging disaster; The switch optimization result includes at least one of the following: the switch state of the line, the load shedding state of the node; the mobile emergency energy storage device scheduling result includes at least one of the following: the scheduling state of the mobile emergency energy storage device between nodes, the arrival time of the mobile emergency energy storage device at the node, and the departure time of the mobile emergency energy storage device at the node.

13. The device according to claim 12, characterized in that The failure probability function of the distribution network equipment during the rainstorm waterlogging disaster is as follows: In the above formula, λ(t) is the failure probability of distribution network equipment during the rainstorm and waterlogging disaster at time t, ε is the initial failure rate, γ is the scale parameter, h(t) is the water depth in the area where the equipment is located at time t, H bw is the critical water depth of the equipment, H w The designed water resistance depth of the distribution station.

14. The device according to claim 13, characterized in that The process of obtaining the water depth in the area where the equipment is located at time t includes: dynamically simulating regional rainfall through the Chicago rain pattern method, calculating the regional water flow process in combination with the two-dimensional surface hydrodynamic model, and obtaining the change of water depth in the area where each equipment is located over time under heavy rain conditions.

15. The device according to claim 12, characterized in that The distribution network reconstruction optimization model includes: a first objective function with the goal of minimizing the amount of load loss, component constraints, topology constraints and power flow constraints.

16. The device according to claim 15, characterized in that The first objective function is as follows: In the above formula, C is the maximum load loss, c load is the unit loss load cost, N bus is a node set, T is the duration of rainstorm waterlogging disaster, Δt is the length of the time interval, is the current active power of the load that can be cut off at node i at time t, c switch is the cost of a single switch operation, N line is a collection of lines, is the switch state of line u at time t, is the switch state of line u at time t-1.

17. The device according to claim 15, characterized in that The element constraints are as follows: In the above formula, is the current active power of the shelvable load on node j at time t, is the total active power of the load that can be cut on node j at time t, is the current reactive power of the shelvable load on node j at time t, is the total reactive power of load that can be cut at node j at time t, T is the duration of the rainstorm disaster, z t is the load shedding state of the node at time t, is the amount of energy stored at node j at time t+1, is the amount of energy stored at node j at time t, is the charging power of the energy storage at node j at time t, h ch is the charging efficiency, Δt is the time interval length, is the discharge power of the energy storage at node j at time t, h dis is the discharge efficiency, E j,bat,max is the maximum energy storage capacity at node j, is the energy storage discharge state, P j,dis,max is the maximum discharge power of energy storage, is the energy storage charging state, P j,ch,max is the maximum charging power of energy storage, N bus A collection of nodes.

18. The device according to claim 15, characterized in that The topological constraints are as follows: In the above formula, N is the existence matrix of distributed power sources in the island area, is the existence coefficient of power source q in the island region at time t, when When , it means that the power supply q belongs to the island area. When , it means that the power source q does not belong to the island area, N C is the available distributed generation set, N T is the set of fault times, M is the connection matrix between distributed generation and nodes, is the connection coefficient between power source q and node i at time t. When , it means that the power supply q is connected to the node i at time t. When , it means that the power supply q is not connected to the node i at time t, N bus is the node set, and T is the duration of the rainstorm waterlogging disaster.

19. The device according to claim 15, characterized in that The power flow constraints are as follows: In the above formula, is the switch state of line u at time t, R is the maximum capacity of the line, is the active power flowing into line u at time t, is the reactive power flowing into line u at time t, is the maximum apparent power flowing into the line, and T is the duration of the rainstorm waterlogging disaster.

20. The device according to claim 12, characterized in that The distribution network fault recovery model includes: a second objective function with the goal of minimizing the mobile resource scheduling cost, a traffic network constraint, a component constraint, a topology constraint and a power flow constraint.

21. The device according to claim 20, characterized in that The second objective function is as follows: In the above formula, A is the total cost, c load is the unit loss load cost, N bus is the node set, T1 is the scheduling duration, Δt is the time interval length, is the total load shedding of node i at time t, R MESS is the mobile resource scheduling cost, λ time is the waiting time cost coefficient, is the dispatching state of the mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node j, is the time it takes for the mobile emergency energy storage device k to arrive at node j, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node i, λ distance is the distance cost coefficient, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, λ penalty is the late penalty cost coefficient, is the late arrival time of mobile emergency energy storage device k at node j.

22. The device according to claim 21, characterized in that The traffic network constraints are as follows: In the above formula, is the attribution coefficient between mobile emergency energy storage device k and node i, when When , it means that the mobile emergency energy storage device k belongs to node i, when When , it means that the mobile emergency energy storage device k does not belong to node i, is the scheduling time of mobile emergency energy storage device k from node i to node j, is the time when the mobile emergency energy storage device k starts serving at node i, c i is the service time of node i, M is a preset constant, us j is the abandonment coefficient of node j, is the time it takes for the mobile emergency energy storage device k to arrive at node i, e i is the starting time of the required recovery time for node i, l i is the end time of the required recovery time of node i, is the delay time of mobile emergency energy storage device k arriving at node i, p j is the power demand of node j, is the power of mobile emergency energy storage device k when it arrives at node j, is the power of the mobile emergency energy storage device k when it arrives at node i, r is the power required to move the emergency energy storage device per unit path, is the travel distance of the mobile emergency energy storage device k after leaving node i at time t, p i is the power recovery requirement of node i, is the waiting time for the mobile emergency energy storage device to replace the battery in the service station at node j, is the maximum capacity of the mobile emergency energy storage device k, is the minimum capacity of the mobile emergency energy storage device k.

23. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for restoring a power distribution system under a rainstorm and waterlogging disaster as described in any one of claims 1 to 11 is implemented.

24. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method for restoring a power distribution system under a rainstorm and waterlogging disaster as claimed in any one of claims 1 to 11 is implemented.

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