A method and system for dispatching main network after disaster repair

Abstract

The present invention discloses a main grid post-disaster maintenance dispatch method and system, which establishes a transmission network post-disaster maintenance team dispatch model based on a risk perception model, a BPR road resistance model, a maintenance team path planning model, a DC power flow model and a component state update model; collects the nodes of the affected components of the power grid after the disaster, inputs the affected component nodes into the transmission network post-disaster maintenance team dispatch model, takes the minimum of the system power shortage and the user's psychological perception loss as the objective function, adopts a genetic algorithm to solve the transmission network post-disaster maintenance team dispatch model to obtain a maintenance dispatch plan, and introduces the implicit value brought by the psychological perception effect into the objective function, while ensuring the efficient completion of the restoration task, comprehensively considers the psychological expectation time of the public in each power outage area and the personality characteristics of the public in each area, and gives priority to restoring the regional load with low tolerance when the power shortage is similar, thereby improving the satisfaction of the power outage users with the power grid post-disaster disposal plan, which is beneficial to improving the service level of the power grid itself; psychological risk perception modeling provides a model basis for the power system to analyze the psychological effects of users.

Description

Technical Field

[0001] The present application belongs to the technical field of main network maintenance, and specifically relates to a method and system for dispatching main network post-disaster maintenance. Background Art

[0002] Since the beginning of the new century, low-probability and high-risk events such as extreme weather have increasingly threatened the power system. Traditional reliability indicators can only cope with "N-1" or "N-2" failures. When extreme events occur, multiple components often fail at the same time, which may lead to large-scale power outages, causing huge economic losses and seriously affecting social stability and people's lives. This poses new challenges to the safety of the power system. Therefore, building a resilient and resilient power grid is of great significance to the power system and national energy security.

[0003] In the face of extreme events, resilient power grids generally minimize the losses caused by extreme events from three aspects: pre-disaster reinforcement, disaster resistance, and post-disaster recovery. In terms of pre-disaster reinforcement, existing research mainly identifies the key components and weak points of the power grid, and then reinforces the key components and weak points at a certain cost. In terms of disaster resistance, the main means of resisting extreme events are network reconstruction, abandoned lines and poles. Although measures such as pre-disaster reinforcement and disaster resistance can greatly reduce the amount of system load loss, power outages in some areas cannot be avoided, and damaged components also need to be repaired by maintenance personnel. In terms of post-disaster recovery, methods such as maintenance team dispatch, maintenance resource scheduling, and coordinated recovery of natural gas and power systems have been formed to restore the transmission network. The power system generally takes the minimum power shortage of the system as the goal under the premise of considering the load level, and uses the TSP model or Dijkstra algorithm to plan the path of the maintenance team according to the traffic conditions of the road, and finally determines the maintenance plan of the maintenance team to quickly restore the transmission network. The power load should be graded according to the requirements for power supply reliability and the degree of political and economic losses or impacts caused by power outages. It can be divided into primary load, secondary load and tertiary load.

[0004] Although the existing maintenance team dispatch method considers the importance of load when formulating maintenance plans, its objective function only considers the explicit economic value brought by the maintenance plan, and does not consider the implicit value brought by the psychological effect of users in the power outage area. This may cause problems such as the low satisfaction of affected users with the power system restoration effect, although the power system has completed the load restoration task. With the improvement of people's living standards and power grid service levels, the psychological effect of users has become important. Therefore, considering the importance of load and the psychological effects of different users in formulating maintenance team dispatch plans can not only ensure the efficient completion of restoration tasks, but also improve the satisfaction of power outage users with the post-disaster disposal plan of the power system. In addition, the traditional TSP model only retains the shortest one when dealing with multiple roads between two nodes, but in the case of road interruption and dynamic road resistance after the disaster, multiple roads must be considered at the same time. Therefore, how to deal with the situation where there are multiple roads between two nodes is also very necessary. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for dispatching post-disaster maintenance of the main network, which overcomes the problem of the coexistence of multiple roads between nodes. When the road is interrupted or the road status changes, the planned path is more reasonable and closer to reality, which can avoid unnecessary detours for the maintenance team, effectively reduce the travel time of the maintenance team on the road, and improve maintenance efficiency.

[0006] A method for dispatching a main network after a disaster, comprising the following steps:

[0007] S1, establish a post-disaster maintenance team dispatch model for the transmission network based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model;

[0008] S2, collects the nodes of the affected components of the power grid after the disaster, inputs the affected component nodes into the transmission network post-disaster maintenance team dispatch model, takes the system power shortage and the user's psychological perception loss as the minimum and takes the genetic algorithm as the objective function to solve the transmission network post-disaster maintenance team dispatch model to obtain the maintenance dispatch plan.

[0009] Furthermore, the objective function is:

[0010]

[0011] Where T is the set of refresh times, B is the node set, W1 is the power shortage weight of the system, W2 is the public time risk perception weight, and w j represents the importance of node j, represents the load shedding of node j at time t, Δt is the time interval, R j is the time risk perception value of the public at node j.

[0012] Furthermore, the risk perception model adopts the risk perception value function R expression:

[0013]

[0014] Among them, the σ and λ distributions represent the degree of convexity of the value functions of gains and losses. Both σ and λ are in the interval [0,1], indicating decreasing sensitivity. The coefficient ρ represents the unequal characteristics of the public's feelings about losses and gains. ρ≥1 means that the public feels more pain when facing losses than happiness when facing gains.

[0015] Furthermore, the BPR road resistance function is used to model the road:

[0016] z=t0(1+α(S) β ) (3)

[0017] Among them, t0 represents the travel time of the road when there is zero traffic flow, S represents the saturation degree of the road, and α and β are undetermined model parameters, which use the values ​​of traffic flow distribution of the US Highway Administration and are taken as 0.15 and 0.4 respectively.

[0018] Furthermore, the TSP model is used to establish a maintenance team dispatch model, with the decision variables as the order in which the maintenance team repairs each component, and the state variables including the path selection of the maintenance team between the components and the time to reach each component.

[0019] Furthermore, the DC power flow model is used to model the operation of the power system, and load shedding is performed by comprehensively considering the component repair status update, load level and public psychological risk perception, so as to minimize the explicit and implicit losses caused by the power outage.

[0020] Furthermore, 1) power balance constraints

[0021]

[0022] where F k，t represents the power flowing through line k at time t, represents the generator output of node j at time t, is the load of node j;

[0023] 2) Line feature constraints

[0024] i, j are the starting and ending nodes of line k (16)

[0025] where u k,t represents the state of line k at time t, X k is the reactance of line k, represents the phase angle of node i connected to line k at time t;

[0026] 3) Line capacity constraints

[0027]

[0028] 4) Load shedding constraints

[0029]

[0030] 5) Generator output constraints

[0031]

[0032] 6) Phase Angle Constraint

[0033]

[0034] Furthermore, the component status update includes determining the specific time of component repair and updating the component status variables:

[0035] The constraints that determine component repair are:

[0036]

[0037]

[0038] where χ j,t It is an auxiliary 0 / 1 variable, representing the transition of the component state;

[0039] Use formula (23) to update the component status

[0040]

[0041] Furthermore, firstly, multiple groups of maintenance plans are randomly generated according to the nodes of the damaged components of the power grid after the disaster, and the encoding strategy of sequential representation is used to encode different maintenance plans;

[0042] The inverse of the objective function is selected as the fitness function to calculate the fitness value of each maintenance plan. The specific form is as follows:

[0043]

[0044] The selection strategy of fitness ratio is adopted to select each scheme. The scheme with large fitness is more likely to be saved, and the scheme with the largest fitness value is saved during selection;

[0045] Randomly select two schemes from multiple groups of maintenance schemes, randomly generate two hybrid points, exchange the corresponding segments of the two schemes, and ensure that the repair order in each scheme adopts a combination of sequential arrangement and random arrangement to prevent falling into local convergence during the solution process;

[0046] For two randomly selected solutions, two fragments in the solution are randomly selected for exchange according to the mutation probability to generate a new solution, and the fitness of the generated new solution is calculated again. Then, the selection, crossover and mutation operations are repeated until the number of iterations t reaches the predetermined value k to form the final output solution.

[0047] A main network post-disaster maintenance dispatch system, comprising a disaster-affected component node collection module and a maintenance dispatch module;

[0048] The affected component node acquisition module is used to obtain the affected component nodes of the power grid after the disaster, and transmit the obtained affected component node information to the maintenance dispatch module;

[0049] The maintenance dispatch module stores the transmission network post-disaster maintenance team dispatch model based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model. It takes the minimum system power shortage and user psychological perception loss as the objective function, and uses a genetic algorithm to solve the transmission network post-disaster maintenance team dispatch model and output a maintenance dispatch plan.

[0050] Compared with the prior art, the present invention has the following beneficial technical effects:

[0051] The present invention discloses a main network post-disaster maintenance dispatch method. The present invention establishes a transmission network post-disaster maintenance team dispatch model based on a risk perception model, a BPR road resistance model, a maintenance team path planning model, a DC power flow model and a component state update model. The power grid post-disaster affected component nodes are collected, and the affected component nodes are input into the transmission network post-disaster maintenance team dispatch model. The system power shortage and user psychological perception loss and minimum are taken as the objective function. The transmission network post-disaster maintenance team dispatch model is solved by a genetic algorithm to obtain a maintenance dispatch plan. The present invention introduces the implicit value brought by the psychological perception effect into the objective function. While ensuring the efficient completion of the restoration task, the psychological expectation time of the public in each power outage area and the personality characteristics of the public in each area are comprehensively considered. When the power shortage is similar, the regional load with low tolerance is restored first, thereby improving the satisfaction of the power outage users with the power grid post-disaster disposal plan, which is beneficial to improving the service level of the power grid itself. The psychological risk perception modeling provides a model basis for the power system to analyze the psychological effects of users.

[0052] Furthermore, the present invention models the public's psychological risk perception based on the utility value function of prospect theory, and integrates behavioral science theory into the power transmission network recovery process after an extreme event. This can avoid unnecessary detours for maintenance teams, effectively reduce the maintenance team's travel time on the road, and improve maintenance efficiency.

[0053] Furthermore, the TSP model of the present invention is modeled based on roads, which overcomes the problem of multiple roads coexisting between nodes. The planned path is more reasonable and closer to reality when the road is interrupted or the road status changes, which can avoid unnecessary detours for the maintenance team, effectively reduce the travel time of the maintenance team on the road, and enable damaged components to be repaired quickly, reducing power outage losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a structural diagram of a transmission network post-disaster maintenance team dispatch model in an embodiment of the present invention.

[0055] Figure 2 This is a time risk perception curve of power outage users in an embodiment of the present invention.

[0056] Figure 3 It is a model flow chart in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only 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 should fall within the scope of protection of the present invention.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0059] like Figure 1 As shown, a method for dispatching a main network after a disaster includes the following steps:

[0060] S1, establish a post-disaster maintenance team dispatch model for the transmission network based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model;

[0061] S2, collects the nodes of the affected components of the power grid after the disaster, inputs the affected component nodes into the transmission network post-disaster maintenance team dispatch model, takes the system power shortage and the user's psychological perception loss as the minimum and takes the genetic algorithm as the objective function to solve the transmission network post-disaster maintenance team dispatch model to obtain the maintenance dispatch plan.

[0062] This application comprehensively considers the importance of loads, public psychological risk perception and system power shortage, and takes the minimization of explicit economic losses caused by power outages and implicit losses caused by public psychological effects as the objective function, that is, the sum of system power shortages and user psychologically perceived losses is taken as the minimum target.

[0063] The utility value function of prospect theory is used to characterize the public's psychological risk perception. The public's psychological expected time is selected as the time reference point. Different reference points of the public will lead to different judgments and decisions of the public for the same result. The public's risk tolerance and risk preference are used to establish the public's time risk perception curve and function model for power restoration time, and introduce them into the objective function. While ensuring the efficient completion of the restoration task, the psychological perception of different users is taken into account, and the user's satisfaction with the power grid restoration plan is improved. The objective function used is:

[0064]

[0065] Where T is the set of refresh times, B is the node set, W1 is the power shortage weight of the system, W2 is the public time risk perception weight, and w j represents the importance of node j, represents the load shedding of node j at time t, Δt is the time interval, R j is the time risk perception value of the public at node j.

[0066] User risk perception model:

[0067] The present invention establishes a psychological risk perception model for power outage users based on the utility value function of prospect theory. The traditional theory of risk decision-making behavior is the expected utility function theory, which assumes that people are all rational people, so the same results have the same impact on the public. Prospect theory is different from the expected utility function theory. It believes that the utility functions pursued by each person are different, and the subjective probabilities of various possibilities are different, which leads to different judgments and decisions. Prospect theory believes that people's decision-making choices depend on the gap between the results and their own expectations, rather than the absolute value of the results themselves. When making decisions, people will have an expectation in their hearts, and judge whether the event is a gain or a loss by comparing the results of the event with their own expectations. The change of the reference point may change the public's feelings about the same thing, which can better reflect people's true psychological feelings.

[0068] The public's risk perception value function R for power restoration time is as follows: Figure 2 As shown in the figure, the horizontal axis is the time of power restoration, and the vertical axis is the time risk perception of power outage users. Figure 2 It can be seen that the time risk perception function R is a piecewise function, which is divided into two sections: 0≤t≤T0 and t≥T0, with the power restoration time T0 expected by the public as the boundary. When t≤T0, the power load recovery time is shorter than the public's psychological expectation. From the perspective of the public in the load recovery area, it can be regarded as a benefit; if t≥T0, it means that the power load recovery is later than the public's psychological expectations, which will cause public psychological dissatisfaction, which can be regarded as a loss. Due to the different degrees of people's perception of losses and gains, the loss curve will be steeper than the gain curve, that is, most people are more sensitive to losses than gains. When the values ​​of losses and gains are equal, the pain caused by losses will be greater than the satisfaction brought by gains. This is called "loss aversion." According to prospect theory, the specific expression of the risk perception value function R is as follows:

[0069]

[0070] Among them, the σ and λ distributions represent the convexity of the value function of gains and losses. Both σ and λ are in the interval [0,1], indicating decreasing sensitivity, while the coefficient ρ represents the unequal characteristics of the public's feelings of loss and gain. ρ≥1 means that the public's pain in the face of loss is much greater than their happiness in the face of gain. The public's risk perception value function is added to the objective function. Since the public in different regions have different tolerance and preference for risks, the maintenance team can combine the importance of the load and give priority to restoring the load in the low-tolerance area while ensuring the rapid load recovery. This can improve the public's overall satisfaction with the post-disaster disposal of the power system.

[0071] The BPR road resistance function is used to establish the road section impedance model to more realistically reflect the traffic conditions;

[0072] The method of the present invention needs to plan the shortest path for the maintenance team to pass between the damaged components. Therefore, it is necessary to quantitatively analyze the road resistance of each road and use the BPR road resistance function to model the road. The specific form is shown in formula (3):

[0073] z=t0(1+α(S) β ) (3)

[0074] Among them, t0 represents the travel time of the road when there is zero traffic flow, S represents the saturation degree of the road, and α and β are undetermined model parameters, which use the values ​​of traffic flow distribution of the US Highway Administration and are taken as 0.15 and 0.4 respectively.

[0075] The TSP model is used to establish a maintenance team dispatch model, considering the coexistence of multiple roads between nodes, to plan the travel path of the maintenance team and determine the time for the maintenance team to repair each damaged component;

[0076] In the maintenance team path planning model, the decision variable is the order in which the maintenance team repairs each component, and the state variables include the path selection between the components and the time to reach each component.

[0077] 1) Determine the maintenance sequence

[0078] The repair team needs to start from the repair station d, reach each damaged component and leave the component after the repair is completed. When the last component is repaired, the repair team returns to the repair station to form a closed loop. A set of 0 / 1 variables is used to represent the repair team's decision:

[0079]

[0080]

[0081]

[0082]

[0083] However, the above model may produce disconnected subgraphs. In order to eliminate disconnected subgraphs, the following constraints need to be added:

[0084] o i -o j +ny ij ≤n-1,i,j∈N∪{d},i≠j (8)

[0085] Where n is the number of damaged components, o i It is the repair sequence number of each damaged component.

[0086] 2) Determine the route of the maintenance team

[0087] In order to take all roads into account, the improved TSP model is used for shortest path planning. Unlike the traditional model, this model uses two sets of 0 / 1 variables representing different directions. and As a decision variable, it can simultaneously consider the coexistence of multiple roads between nodes, and can better cope with road interruptions and changes in road conditions:

[0088]

[0089] Where Z ij represents the time required to go from node i to node j, z l is the road resistance of road l.

[0090] In route planning, the maintenance team will not pass the same road twice, that is, the maintenance team will only pass a road in one direction:

[0091]

[0092] Where L is the set of all roads.

[0093] Each path planning needs to ensure that the maintenance team starts from node i and arrives at node j, and must leave from the intermediate node after arriving at the intermediate node:

[0094]

[0095]

[0096] where r c Represents the set of lines connected to point c.

[0097] 3) Time of arrival at each affected component:

[0098] The time to reach each damaged component consists of two parts. The first part is the time the repair team sets out from the last damaged component, and the second part is the time the repair team takes to reach the damaged component. The first part is composed of the time the repair team reaches the last damaged component and the time it takes to repair the last damaged component:

[0099]

[0100]

[0101] in is the time to leave the damaged component i, is the time to reach damaged element i, The time required to repair the damaged component i, M is a large number.

[0102] Use the DC power flow model to model the operation of the power system, comprehensively consider the component repair status update, load level and public psychological risk perception to shed load, and minimize the explicit and implicit losses caused by power outages;

[0103] DC power flow model:

[0104] The present invention is aimed at the restoration of the power transmission network after a disaster, and only focuses on active power, so the DC power flow model is used to model the power transmission network:

[0105] 1) Power balance constraints

[0106]

[0107] where Fk,t represents the power flowing through line k at time t, represents the generator output of node j at time t, is the load of node j.

[0108] 2) Line feature constraints

[0109] i, j are the starting and ending nodes of line k (16)

[0110] where u k,t represents the state of line k at time t, X k is the reactance of line k, Represents the phase angle of node i connected to line k at time t.

[0111] 3) Line capacity constraints

[0112]

[0113] 4) Load shedding constraints

[0114]

[0115] 5) Generator output constraints

[0116]

[0117] 6) Phase Angle Constraint

[0118]

[0119] i, j are the starting node and the ending node of line k.

[0120] Component status update model:

[0121] The component status update consists of two parts. The first part is to determine the specific time of component repair, and the second part is to update the component's state variables. The constraints for determining component repair are:

[0122]

[0123]

[0124] where χ j,t It is an auxiliary 0 / 1 variable that represents the transition of the component state.

[0125] Use formula (23) to update the component status

[0126]

[0127] The present invention uses a genetic algorithm to solve the established model. The process of using the genetic algorithm to solve the model is as follows: Figure 3 As shown:

[0128] Firstly, multiple groups of maintenance plans are randomly generated according to the nodes of the damaged components after the power grid disaster, and the different maintenance plans are encoded using the coding strategy of sequential representation;

[0129] The inverse of the objective function is selected as the fitness function to calculate the fitness value of each maintenance plan. The specific form is as follows:

[0130]

[0131] The selection strategy of fitness ratio is adopted to select each scheme. The scheme with large fitness is more likely to be saved, and the scheme with the largest fitness value is saved during selection;

[0132] Randomly select two schemes from multiple groups of maintenance schemes, randomly generate two hybrid points, exchange the corresponding segments of the two schemes, and ensure that the repair order in each scheme adopts a combination of sequential arrangement and random arrangement to prevent falling into local convergence during the solution process;

[0133] For two randomly selected solutions, two fragments in the solution are randomly selected for exchange according to the mutation probability to generate a new solution, and the fitness of the generated new solution is calculated again. Then, the selection, crossover and mutation operations are repeated until the number of iterations t reaches the predetermined value k to form the final output solution.

[0134] In one embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, 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 adopts a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the main network post-disaster maintenance dispatch method.

[0135] A main network post-disaster maintenance dispatch system, comprising a disaster-affected component node collection module and a maintenance dispatch module;

[0136] The affected component node acquisition module is used to obtain the affected component nodes of the power grid after the disaster, and transmit the obtained affected component node information to the maintenance dispatch module;

[0137] The maintenance dispatch module stores the transmission network post-disaster maintenance team dispatch model based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model. It takes the minimum system power shortage and user psychological perception loss as the objective function, and uses a genetic algorithm to solve the transmission network post-disaster maintenance team dispatch model and output a maintenance dispatch plan.

[0138] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. The computer-readable storage medium includes a built-in storage medium in the terminal device, which provides a storage space and stores the operating system of the terminal, and may also include an extended storage medium supported by the terminal device. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory (Non-volatile memory), such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for post-disaster maintenance dispatch of the main network in the above embodiment.

[0139] The present invention takes the dispatch of post-disaster maintenance teams of power transmission networks as the research object, introduces public psychological factors into the objective function based on prospect theory, uses the TSP model for path planning, and establishes a post-disaster maintenance team dispatch model based on the DC power flow model. This method models the public's psychological risk perception based on the utility value function of prospect theory, integrates behavioral science theory into the process of power transmission network restoration after extreme events, and introduces the implicit value brought by the public's psychological perception effect into the objective function. While ensuring the efficient completion of the restoration task, it comprehensively considers the psychological expectation time of the public in each power outage area and the personality characteristics of the public in each region. When the power shortage is similar, the regional load with low tolerance is restored first, thereby improving the satisfaction of power outage users with the post-disaster disposal plan of the power grid, which is conducive to improving the service level of the power grid itself; psychological risk perception modeling provides a model basis for the power system to analyze the psychological effects of users.

[0140] The method of the present invention uses the TSP model to build a model based on roads, which overcomes the problem of multiple roads coexisting between nodes. The planned path is more reasonable and closer to reality when the road is interrupted or the road status changes, which can avoid unnecessary detours for the maintenance team, effectively reduce the travel time of the maintenance team on the road, and enable damaged components to be repaired quickly, reducing power outage losses. The TSP model of the present application is an improved TSP model, which provides an effective tool for formulating reasonable maintenance plans for power systems.

Claims

1. A method for dispatching a main network after a disaster, characterized in that: The following steps are involved: S1, establish a post-disaster maintenance team dispatch model for the transmission network based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model; S2, collect the nodes of the affected components after the power grid is hit, input the affected component nodes into the transmission network post-disaster maintenance team dispatch model, take the system power shortage and the user psychological perception loss as the minimum and take the genetic algorithm as the objective function to solve the transmission network post-disaster maintenance team dispatch model to obtain the maintenance dispatch plan; The risk perception model adopts the risk perception value function R expression: Among them, the σ and λ distributions represent the convexity of the value functions of gains and losses. Both σ and λ are in the interval [0,1], indicating decreasing sensitivity. The coefficient ρ represents the unequal characteristics of the public's feelings about losses and gains. ρ≥1 means that the public feels more pain in the face of losses than happiness in the face of gains. The BPR road resistance function is used to model the road: z=t0(1+α(S) β ) (3) Among them, t0 represents the travel time of the road when there is no traffic flow, S represents the saturation of the road, and the parameters of the α and β models are unknown. The values ​​of the traffic flow distribution of the US Highway Administration are used, which are 0.15 and 0.4 respectively; The TSP model is used to establish a maintenance team dispatch model, with the decision variable being the order in which the maintenance team repairs each component, and the state variables including the path selection between components and the time to reach each component. The DC power flow model is used to model the operation of the power system, and load shedding is performed by comprehensively considering the component repair status update, load level and public psychological risk perception, so as to minimize the explicit and implicit losses caused by power outages.

2. A method for dispatching a main network after a disaster according to claim 1, characterized in that: The objective function is: Where T is the set of refresh times, B is the node set, W1 is the power shortage weight of the system, W2 is the public time risk perception weight, and w j represents the importance of node j, represents the load shedding of node j at time t, Δt is the time interval, R j is the time risk perception value of the public at node j.

3. A method for dispatching a main network after a disaster according to claim 1, characterized in that: 1) Power balance constraints Among them, F k,t represents the power flowing through line k at time t, represents the generator output of node j at time t, is the load of node j; 2) Line feature constraints where u k,t represents the state of line k at time t, X k is the reactance of line k, represents the phase angle of node i connected to line k at time t; 3) Line capacity constraints 4) Load shedding constraints 5) Generator output constraints 6) Phase Angle Constraint 4. A method for dispatching a main network after a disaster according to claim 1, characterized in that: Component status update includes determining the specific time of component repair and updating the component's status variables: The constraints that determine component repair are: where χ j,t It is an auxiliary 0 / 1 variable, representing the transition of the component state; Use formula (23) to update the component status 5. A method for dispatching a main network after a disaster according to claim 1, characterized in that: Firstly, multiple groups of maintenance plans are randomly generated according to the nodes of the damaged components after the power grid disaster, and the different maintenance plans are encoded using the coding strategy of sequential representation; The inverse of the objective function is selected as the fitness function to calculate the fitness value of each maintenance plan. The specific form is as follows: The selection strategy of fitness ratio is adopted to select each scheme. The scheme with large fitness is more likely to be saved, and the scheme with the largest fitness value is saved during selection; Randomly select two schemes from multiple groups of maintenance schemes, randomly generate two hybrid points, exchange the corresponding segments of the two schemes, and ensure that the repair order in each scheme adopts a combination of sequential arrangement and random arrangement to prevent falling into local convergence during the solution process; For two randomly selected solutions, two fragments in the solution are randomly selected for exchange according to the mutation probability to generate a new solution, and the fitness of the generated new solution is calculated again. Then, the selection, crossover and mutation operations are repeated until the number of iterations t reaches the predetermined value k to form the final output solution.

6. A main network post-disaster maintenance dispatch system, characterized in that: It includes the affected component node collection module and the maintenance dispatch module; The affected component node acquisition module is used to obtain the affected component nodes of the power grid after the disaster, and transmit the obtained affected component node information to the maintenance dispatch module; The maintenance dispatch module stores the transmission network post-disaster maintenance team dispatch model based on the risk perception model, BPR road resistance model, maintenance team path planning model, DC power flow model and component status update model. It takes the minimum system power shortage and user psychological perception loss as the objective function, and uses genetic algorithm to solve the transmission network post-disaster maintenance team dispatch model and output the maintenance dispatch plan. The risk perception model adopts the risk perception value function R expression: Among them, the σ and λ distributions represent the convexity of the value functions of gains and losses. Both σ and λ are in the interval [0,1], indicating decreasing sensitivity. The coefficient ρ represents the unequal characteristics of the public's feelings about losses and gains. ρ≥1 means that the public feels more pain in the face of losses than happiness in the face of gains. The BPR road resistance function is used to model the road: z=t0(1+α(S) β ) (3) Among them, t0 represents the travel time of the road when there is no traffic flow, S represents the saturation of the road, and the parameters of the α and β models are unknown. The values ​​of the traffic flow distribution of the US Highway Administration are used, which are 0.15 and 0.4 respectively; The TSP model is used to establish a maintenance team dispatch model, with the decision variable being the order in which the maintenance team repairs each component, and the state variables including the path selection between components and the time to reach each component. The DC power flow model is used to model the operation of the power system, and load shedding is performed by comprehensively considering the component repair status update, load level and public psychological risk perception, so as to minimize the explicit and implicit losses caused by power outages.

Images