Emergency insurance supply and fault repair collaborative optimization method for power distribution network

Abstract

The invention provides a power distribution network emergency supply guarantee and fault repair collaborative optimization method, and the method comprises the steps: solving and executing an emergency supply guarantee scheme of an emergency power supply period based on a real-time power grid state; wherein the solution of the emergency supply guarantee scheme of the emergency power supply cycle comprises the following steps: determining micro-grid isolated island operation, power grid conversion, flexible load shedding and distributed power generation adjustment strategies by taking the maximum load recovery amount and reconstruction cost as targets; updating the real-time power grid state, judging whether all fault points are repaired at the beginning of each time period of the first-aid repair recovery cycle, and if so, ending the repair; otherwise, solving and executing a first-aid repair recovery scheme; wherein the solution of the first-aid repair and recovery scheme comprises the steps of planning a driving route of a maintenance team and adjusting resource scheduling by taking the minimization of expected travel time and power failure loss in a traffic network, the optimization of economical efficiency and the maximization of the utilization of a distributed power generation unit as targets. The method can improve the fault repair effect of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of distribution network fault repair, and in particular to a method for collaborative optimization of distribution network emergency supply guarantee and fault repair. Background Art

[0002] With the rapid development of my country's economy and the growing demand for electricity, the stable operation of distribution networks is crucial for ensuring social production and daily life. However, distribution networks are inevitably affected by factors such as natural disasters, equipment aging, and human damage, leading to failures. Fault repair and service restoration are key aspects of distribution network operation and management, directly impacting power supply reliability, user satisfaction, and the economic benefits of power grid companies.

[0003] At present, there are mainly the following methods for distribution network fault repair: (1) Sequential optimization method: After a fault occurs, fault location, fault isolation, load transfer and repair work are carried out in sequence. This method ignores the mutual influence between each link, resulting in a long repair time and low power supply reliability. (2) Static collaborative optimization method: Before the fault occurs, a fixed fault handling plan is formulated. When a fault occurs, operations are carried out according to the preset plan. However, this method cannot adapt to the changes in the real-time operating status of the distribution network, and it is difficult to achieve optimal fault repair and service recovery. (3) Single-stage optimization method: The fault repair and service recovery process is regarded as a whole, and a single-stage optimization model is used for solution. Although the optimization effect is improved to a certain extent, it fails to fully consider the dynamics and uncertainty of distribution network fault repair.

[0004] In view of the technical problems existing in the existing technology, it is necessary to design a new distribution network fault repair method. Summary of the Invention

[0005] The purpose of this application is to provide a method for collaborative optimization of emergency power supply and fault repair in distribution networks, which can improve the effect of distribution network fault repair.

[0006] To achieve the purpose of the present invention, the technical solution provided in this application is:

[0007] A method for collaborative optimization of emergency power supply and fault repair in a distribution network, comprising:

[0008] Q1. Obtain real-time grid status;

[0009] Q3. Calculate and execute an emergency power supply guarantee plan for the emergency power supply period based on the real-time grid status. This plan includes determining strategies for microgrid islanding, grid switching, flexible load shedding, and distributed generation adjustment, with the goal of maximizing load restoration and reconstruction costs.

[0010] Q3. Update the real-time grid status. At the beginning of each time period of the emergency repair and restoration cycle, determine whether all fault points have been repaired. If so, end the repair; otherwise, execute step Q4;

[0011] Q4. Solve and execute the emergency repair and restoration plan, and return to step Q3; wherein, the solving of the emergency repair and restoration plan includes: planning the maintenance team's travel route and adjusting resource scheduling with the goal of minimizing the expected travel time and power outage losses in the transportation network, optimizing economic efficiency and maximizing the utilization of distributed power generation units.

[0012] In a possible implementation, in step Q3, the objective function of solving the emergency supply guarantee solution for the emergency power supply period is:

[0013]

[0014]

[0015]

[0016] in, is the objective function value, and Load recovery and reconstruction costs The weight coefficient of is the total number of nodes in the distribution network; is the total number of lines in the distribution network; the emergency power supply cycle is divided into The length of each time period can be determined artificially according to the size of the distribution network. Generally speaking, the larger the distribution network, the longer the emergency power supply cycle. The bigger; Representation node In the time period The connection status within, if the node exist If the time period is connected to the grid, the value is 1, otherwise it is 0; For the The weight of the load on each node (importance coefficient); It is in the time period Internal Node Load power demand on 、 and are the corresponding cost coefficients for load reduction, distributed generation units and switching operations, It is in the time period Internal Node The load reduction on It is in the time period Internal Node Output of distributed generation units on It's a line In the time period The connection status at the beginning, if connected, it is 1, if disconnected, it is 0; line It refers to the node With node The lines between.

[0017] In a possible implementation, in step Q3, solving the constraint conditions of the emergency supply guarantee solution for the emergency power supply period includes:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] in, Is a binary variable, indicating the Are nodes divided into islands? ,like Indicates the Nodes are divided into islands ,like Indicates the Nodes not assigned to islands ; is the collection of all islands, and They are the important load set and the distributed generation unit set; Representation node belong and does not belong to the set ; Indicates an island with black start, frequency regulation and voltage regulation capabilities The connection status of the potential root node, if connected is 1, if disconnected is 0; Is a binary variable representing the line Is it included in the island? If so, ,otherwise ; It's a loop The number of lines included, is a collection of cycles, and is the number of branches in each loop.

[0025] In one possible implementation, in step Q4, solving and executing the emergency repair and restoration plan includes: establishing a solution to the emergency repair and restoration plan, including: establishing an upper-level model and a lower-level model to solve the emergency repair and restoration plan; wherein the upper-level model is used to plan the travel route of the maintenance team with the goal of minimizing the expected travel time and power outage losses in the traffic network; and the lower-level model is used to adjust resource scheduling with the goal of optimizing economic efficiency and maximizing the utilization of distributed power generation units.

[0026] In one possible implementation, the objective function of the upper model is:

[0027]

[0028]

[0029]

[0030] in, and are the expected travel times in the transportation network and power outage losses The weight coefficient of It is an emergency center The maintenance team, It's the maintenance team driving routes; is an edge in the transportation network topology; It's the edge expected travel time; is a set of time period numbers; Representation node In the time period Active power within; A set of fault point numbers; By the fault point The set of nodes that caused the power outage; is a node The load demand on is a node Priority weight of upper load; Represents a binary variable, representing the fault point In the time period Is it repaired? If the fault point In the time period If the internal has been repaired, the value is 1, otherwise it is 0; and are the weight coefficients of the power outage loss of all loads and the power outage loss caused by the fault point, respectively.

[0031] In one possible implementation, the objective function of the lower model is:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] in, and Economic objectives and the use of distributed generation units The weight coefficient of Distributed Generation Unit In the time period Internal efforts, Distributed Generation Unit Revenue function per unit output; Is connected to the node A collection of distributed generation units on 、 and is the weight coefficient, which is used to distinguish the importance of different loads; is a node Priority weight of upper load; By the fault point The set of nodes that are out of power due to It is the failure point The time when the repair task was completed; It is a set of nodes with three levels of transferable load; and are the end and start time periods of the acceptable transfer interval, respectively; is a node Unit dispatch cost of transferable load; and is a node In the time period Transferable load power before and after internal dispatch; Indicates the time period Distributed power generation units Unit output income; It is a collection of energy storage; Indicates time period Energy Storage Unit discharge power gain, It is energy storage In the time period Discharge power within It is a collection of electric vehicles; Indicates time period electric vehicles The unit discharge power gain, It's an electric car In the time period Discharge power within Indicates time period electric vehicles The unit charging power cost, It's an electric car In the time period Charging power within Indicates time period Energy Storage The unit charging power cost, It is energy storage In the time period Charging power within Shows the distribution network and transmission network in the time period The unit power interaction cost when The time period between the transmission network and the distribution network The interaction power within Indicates the distribution network and microgrid in the time period The unit power interaction cost within Is the time period between the microgrid and the distribution network Interaction power within; Is a binary variable representing the fault point In the time period Is it repaired? If the fault point In the time period Not repaired inside ,otherwise .

[0038] In one possible implementation, the constraints of the lower model include:

[0039] Important load priority constraints:

[0040]

[0041] in, For nodes The minimum active power requirement;

[0042] Distributed generation unit priority scheduling constraints:

[0043]

[0044] in, It is the priority scheduling coefficient, ranging from 0 to 1. The larger it is, the higher the scheduling priority. It is a distributed power generation unit Maximum capacity;

[0045] Energy storage constraints:

[0046]

[0047]

[0048]

[0049] in, Represents energy storage In the time period The amount of electricity inside; For time period length; For energy storage In the time period Charging time within The proportion of For energy storage In the time period The discharge time within The proportion of , energy storage In the time period The total charge and discharge time is less than ; It is energy storage The maximum charging power, It is energy storage Maximum discharge power;

[0050] Electric vehicle constraints:

[0051]

[0052]

[0053]

[0054] in, Representing electric vehicles In the time period The battery level at the start; For time period length; For electric vehicles In the time period Charging time within The proportion of For electric vehicles In the time period The discharge time within proportion; , electric vehicles In the time period The total charge and discharge time is less than ; It's an electric car The maximum charging power, It's an electric car Maximum discharge power;

[0055] Interaction constraints between microgrid and distribution network:

[0056]

[0057] in, Represents the set of nodes in the microgrid; Represents the collection of distributed generation units within the microgrid; Representation node In the time period The load power within Indicates the time period between the microgrid and the distribution network Interaction power within;

[0058] Interaction constraints between transmission and distribution networks:

[0059]

[0060] in, Indicates time period The maximum interactive power between the internal transmission grid and the distribution grid, Indicates time period Interaction power between the internal transmission grid and the distribution grid;

[0061] Microgrid autonomous operation constraints:

[0062]

[0063] in, It is a microgrid Electric car collection inside, microgrids In the time period The amount of energy stored in it.

[0064] Beneficial effects:

[0065] The present invention proposes a collaborative optimization method for emergency power supply and fault repair in distribution networks, which realizes rapid power supply restoration and efficient fault repair through a two-stage model. In the first stage, the power supply of important loads is quickly restored through island division and flexible load reduction; in the second stage, the repair time and power outage losses are minimized and the utilization rate and economy of distributed power generation units are maximized by combining maintenance path optimization and distributed resource scheduling. Through two-stage collaborative optimization, the fault repair time is significantly shortened and the power outage losses are reduced; the power supply reliability is improved by dynamically adjusting the strategy to adapt to the real-time operating status; and the global optimal fault repair strategy is achieved by comprehensively considering multi-dimensional constraints such as transportation networks, distributed power sources, energy storage and microgrid interactions. The present invention can significantly improve the efficiency and economy of post-disaster recovery of distribution networks. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution of the present application will be further described in detail below in conjunction with the embodiments of the present application.

[0067] The present application embodiment proposes a method for collaboratively optimizing emergency power supply and fault repair in a distribution network, including:

[0068] Q1. Obtain real-time grid status;

[0069] In some embodiments, the grid status includes basic parameters and fault information, currently available power resources and load demand;

[0070] Q2. Calculate and execute an emergency power supply guarantee plan for the emergency power supply period based on the real-time grid status. This plan includes determining strategies for microgrid islanding, grid switching, flexible load shedding, and distributed generation adjustment, with the goal of maximizing load restoration and reconstruction costs.

[0071] The collaborative optimization of this application includes two stages: the first stage is the emergency power supply stage, and the second stage is the emergency repair and restoration stage.

[0072] In the first stage, the control center quickly determines the microgrid island operation based on various information (such as optimizing the following objective functions and constraints). 、 、 ), grid conversion (such as optimizing the following objective functions and constraints ), flexible load shedding (such as optimizing the following objective functions and constraints ) and distributed generation adjustment strategies (such as optimizing the following objective functions and constraints ), and sends operating instructions to the corresponding switches to implement inversion and other operations to maintain basic power supply capacity.

[0073] In some embodiments, the objective function for solving the emergency supply guarantee solution is as follows:

[0074] ( )

[0075] (2)

[0076] (3)

[0077] in, is the objective function value, and Load recovery and reconstruction costs The weight coefficient of is the total number of nodes in the distribution network; is the total number of lines in the distribution network; the emergency power supply cycle is divided into The length of each time period can be determined artificially according to the size of the distribution network. Generally speaking, the larger the distribution network, the longer the emergency power supply cycle. The bigger; Representation node In the time period The connection status within, if the node exist If the time period is connected to the grid, the value is 1, otherwise it is 0; For the The weight of the load on each node (importance coefficient); It is in the time period Internal Node Load power demand on 、 and are the corresponding cost coefficients for load reduction, distributed generation units and switching operations, It is in the time period Internal Node The load reduction on It is in the time period Internal Node Output of distributed generation units on It's a line In the time period The connection status at the beginning, if connected, it is 1, if disconnected, it is 0; line It refers to the node With node The lines between.

[0078] Constraints include:

[0079] (4)

[0080] (5)

[0081] (6)

[0082] (7)

[0083] (8)

[0084] (9)

[0085] in, Is a binary variable, indicating the Are nodes divided into islands? ,like Indicates the Nodes are divided into islands ,like Indicates the Nodes not assigned to islands ; Formula (4) means that in the set Each node in must be divided into an island, is the collection of all islands, and They are the set of important loads (important load nodes) and the set of distributed generation units (distributed generation unit nodes); Representation node belong and does not belong to the set , Formula (5) shows that for any , is excluded from the island; Formula (6) means that for any island , which contains at least one important load and one distributed generation unit to maintain normal operation, Indicates an island with black start, frequency regulation and voltage regulation capabilities The connection status of the potential root node, if connected is 1, if disconnected is 0; Is a binary variable representing the line Is it included in the island? If so, ,otherwise ; Formula (7) shows that only when the line line The starting and ending nodes are divided into isolated islands When the line Only included in the island middle; It's a loop The number of lines included, formula (8) indicates that there is no loop in each island, is a collection of cycles, and is the number of branches in each loop, and formula (9) indicates that each node and line in the The connection status within a time period.

[0086] Q3. Update the real-time grid status. At the beginning of each time period of the emergency repair and restoration cycle, determine whether all fault points have been repaired. If so, end the repair; otherwise, execute step Q4;

[0087] Q4. Solve and execute the emergency repair and restoration plan; return to step Q3 until all fault points have been repaired; wherein, the emergency repair and restoration plan includes: planning the maintenance team's travel route and adjusting resource scheduling with the goals of minimizing the expected travel time and power outage losses in the transportation network, optimizing economic efficiency, and maximizing the utilization of distributed power generation units.

[0088] The second stage is the emergency repair and restoration stage. In this stage, a two-layer model is established, namely the upper model and the lower model. The driving route of the maintenance team is planned by comprehensively considering the geographical distance from the emergency center to the fault point, power outage losses, resource availability and other constraints (such as optimizing the following objective function and constraints). ) and adjust resource scheduling (such as optimizing the following objective functions and constraints 、 、 、 、 、 、 ).

[0089] The upper model aims to minimize the expected travel time and power outage loss in the transportation network. The objective function is as follows:

[0090] (10)

[0091] (11)

[0092] (12)

[0093] in, and are the expected travel times in the transportation network and power outage losses The weight coefficient of It is an emergency center The maintenance team, It's the maintenance team driving routes; is an edge in the transportation network topology; It's the edge expected travel time; is the node number, is a set of node numbers, is the time period number, It is a collection of time period numbers. The emergency repair and recovery period is divided into time periods, for The number of time period numbers in , the length of each time period is , Experience points available; Representation node In the time period Active power within; A set of fault point numbers; By the fault point The set of nodes that caused the power outage; is a node The load demand on is a node Priority weight of upper load; Represents a binary variable, representing the fault point In the time period Is it repaired? If the fault point In the time period If the internal has been repaired, the value is 1, otherwise it is 0; and are the weight coefficients of the power outage loss of all loads and the power outage loss caused by the fault point respectively; the first term of formula (12) represents the power outage loss of all loads, and the second term represents the power outage loss caused by the fault point. The first term sums the power demand of all nodes and nodes in the disconnected state in all time periods, and the second term sums the power of all fault points, all time periods and all power-off nodes, and calculates the power of all nodes through the weights. Distinguish the importance of loads on different nodes. Formula (12) uses the weight coefficient 、 The power outage loss of all loads and the power outage loss caused by the fault point are comprehensively considered, and the weight Distinguish the importance of loads at different nodes, and focus on reducing the power outage losses of important loads while considering the power outage losses of all loads, so as to more comprehensively evaluate the impact of the fault point on the distribution network.

[0094] Furthermore, the three-point method is used to estimate the edge Expected travel time .

[0095] Furthermore, the constraints of the upper model include emergency repair path constraints, emergency repair capacity constraints, and emergency repair resource constraints.

[0096] The lower model aims to optimize economic performance and maximize the utilization of distributed generation units. The objective function is as follows:

[0097] (13)

[0098] (14)

[0099] (15)

[0100] (16)

[0101] (17)

[0102] in, and Economic objectives and the use of distributed generation units The weight coefficient of Distributed Generation Unit In the time period Internal efforts, Distributed Generation Unit Revenue function per unit output; Is connected to the node A collection of distributed generation units on 、 and is a weight coefficient used to distinguish the importance of different loads, and in some embodiments, can be 1, 0.2, and 0.2 respectively; is a node Priority weight of upper load; By the fault point The set of nodes that are out of power due to It is the failure point The time when the repair task was completed; It is a set of nodes with three levels of transferable load; and are the end and start time periods of the acceptable transfer interval, respectively; is a node The unit dispatch cost of the transferable load may be set to RMB 2 per kilowatt-hour in some embodiments; and is a node In the time period Transferable load power before and after internal dispatch; Indicates the time period Distributed power generation units Unit output income; It is a collection of energy storage; Indicates time period Energy Storage Unit discharge power gain, It is energy storage In the time period Discharge power within It is a collection of electric vehicles; Indicates time period electric vehicles The unit discharge power gain, It's an electric car In the time period Discharge power within Indicates time period electric vehicles The unit charging power cost, It's an electric car In the time period Charging power within Indicates time period Energy Storage The unit charging power cost, It is energy storage In the time period Charging power within Shows the distribution network and transmission network in the time period The unit power interaction cost when The time period between the transmission network and the distribution network The interaction power within Indicates the distribution network and microgrid in the time period The unit power interaction cost within Is the time period between the microgrid and the distribution network Interaction power within; Is a binary variable representing the fault point In the time period Is it repaired? If the fault point In the time period Not repaired inside ,otherwise ; Right now The purpose of formula (15) is to maximize the economic efficiency, where the + term is the income from the operation of the distribution network, and the - term is the cost of the operation of the distribution network. The purpose of formula (14) is to maximize the utilization of distributed generation units.

[0103] The lower-level model considers transferable load constraints, radial topology constraints, distributed generation unit power constraints, power flow balance constraints, line and load status constraints, repair and system operation status coupling constraints, distribution line power constraints, and the following constraints:

[0104] Important load priority constraints:

[0105] (18)

[0106] in, For nodes The minimum active power requirement of the load is , and formula (18) ensures that the active power of the important load must be greater than or equal to its minimum requirement.

[0107] Distributed generation unit priority scheduling constraints:

[0108] (19)

[0109] in, It is the priority scheduling coefficient, ranging from 0 to 1. The larger it is, the higher the scheduling priority. Is a distributed power generation unit (such as distributed photovoltaic) The maximum capacity of , formula (19) enables distributed generation units to be dispatched first.

[0110] Energy storage constraints:

[0111] (20)

[0112] (twenty one)

[0113] (twenty two)

[0114] in, Represents energy storage In the time period The amount of electricity inside; For time period length; For energy storage In the time period Charging time within The ratio of ), For energy storage In the time period The discharge time within The ratio of ), , energy storage In the time period The total charge and discharge time is less than ; It is energy storage The maximum charging power, It is energy storage The maximum discharge power of , formula (20) represents the energy storage power balance, formulas (21) and (22) are the charge and discharge power limits of the energy storage;

[0115] Electric vehicle constraints:

[0116] (twenty three)

[0117] (twenty four)

[0118] (25)

[0119] in, Representing electric vehicles In the time period The battery level at the start; For time period length; For electric vehicles In the time period Charging time within The ratio of ), For electric vehicles In the time period The discharge time within The ratio of ); , electric vehicles In the time period The total charge and discharge time is less than ; It's an electric car The maximum charging power, It's an electric car The maximum discharge power of the electric vehicle is given by formula (23), and formulas (24) and (25) are the charge and discharge power limits of the electric vehicle.

[0120] Interaction constraints between microgrid and distribution network:

[0121] (26)

[0122] in, Represents the set of nodes in the microgrid; Represents the collection of distributed generation units within the microgrid; Representation node In the time period The load power within Indicates the time period between the microgrid and the distribution network The interactive power within.

[0123] Interaction constraints between transmission and distribution networks:

[0124] (27)

[0125] in, Indicates time period The maximum interactive power between the internal transmission grid and the distribution grid, Indicates time period The interaction power between the transmission grid and the distribution grid.

[0126] Explanation of the function of the formula: Due to the failure of the distribution network, the power supply of a large number of loads in the distribution network cannot be guaranteed. The present invention considers dispatching flexible resources such as distributed generation, electric vehicles, and energy storage, and interacting with the main grid and microgrid to ensure the power supply of these loads caused by power outages due to the failure. The function of formulas (20)-(25) is to constrain the charging and discharging power of flexible resources to not exceed their maximum values. The function of formula (26) is to ensure that the remaining power is transmitted to the distribution network while ensuring that the needs of the microgrid itself are met. The function of formula (27) is to ensure that the power transmitted from the main grid to the distribution network does not exceed the maximum transmission power.

[0127] Microgrid autonomous operation constraints:

[0128] (28)

[0129] in, It is a microgrid Electric car collection inside, microgrids In the time period The amount of energy stored in it.

[0130] The embodiment of the present application establishes a two-stage emergency supply guarantee and fault repair collaborative model. In the first stage, with the goal of maximizing load recovery and reconstruction cost, the microgrid island operation, grid conversion, flexible load reduction and distributed power generation adjustment strategy are determined. In the second stage, the economic benefits of the distribution network and the utilization of distributed generation units are maximized, and the travel time of maintenance personnel in the traffic network and the power outage losses caused by the fault point are minimized. Considering real-time traffic conditions, travel time and emergency resource constraints, important load priority restoration constraints, distributed generation unit priority scheduling, distribution network and transmission network and microgrid interaction, new grid-connected entities and other constraints, a method for optimally scheduling maintenance personnel after extreme disasters is proposed, and the repair strategy is dynamically modified according to the process of repair work. By adjusting the optimization objectives and constraints in real time, efficient coordination of emergency supply guarantee and fault repair of the distribution network is achieved. Taking into account the mutual influence between each link, the repair time is accelerated, and the power supply reliability is improved. At the same time, the changes in the real-time operating status of the distribution network, the interaction between the main distribution and microgrid layers, the influence of distributed generation units, energy storage, and electric vehicles are considered, and the dynamic and uncertainty of distribution network fault repair are fully considered. This application can improve the speed of distribution network fault processing, reduce power outage losses, improve power supply reliability, and provide strong support for the stable operation of my country's distribution network.

[0131] The foregoing description is merely a preferred embodiment of the present application and is not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for collaborative optimization of emergency supply and fault repair in a distribution network, characterized in that: include: Q1. Obtain real-time grid status; Q3. Calculate and execute an emergency power supply guarantee plan for the emergency power supply period based on the real-time grid status. This plan includes determining strategies for microgrid islanding, grid switching, flexible load shedding, and distributed generation adjustment, with the goal of maximizing load restoration and reconstruction costs. Q3. Update the real-time grid status. At the beginning of each time period of the emergency repair and restoration cycle, determine whether all fault points have been repaired. If so, end the repair; otherwise, execute step Q4; Q4. Solve and execute the emergency repair and restoration plan, and return to step Q3; wherein, the solving of the emergency repair and restoration plan includes: planning the maintenance team's travel route and adjusting resource scheduling with the goal of minimizing the expected travel time and power outage losses in the transportation network, optimizing economic efficiency and maximizing the utilization of distributed power generation units.

2. The method according to claim 1, characterized in that In step Q3, the objective function of solving the emergency supply guarantee solution for the emergency power supply period is: ； ； ； in, is the objective function value, and Load recovery and reconstruction costs The weight coefficient of is the total number of nodes in the distribution network; is the total number of lines in the distribution network; the emergency power supply cycle is divided into The length of each time period can be determined artificially according to the size of the distribution network. Generally speaking, the larger the distribution network, the longer the emergency power supply cycle. The bigger; Representation node In the time period The connection status within, if the node exist If the time period is connected to the grid, the value is 1, otherwise it is 0; For the The weight of the load on each node; It is in the time period Internal Node Load power demand on 、 and are the corresponding cost coefficients for load reduction, distributed generation units and switching operations, It is in the time period Internal Node The load reduction on It is in the time period Internal Node Output of distributed generation units on It's a line In the time period The connection status at the beginning, if connected, it is 1, if disconnected, it is 0; line It refers to the node With node The lines between.

3. The method according to claim 1, characterized in that In step Q3, solving the constraint conditions of the emergency supply guarantee solution for the emergency power supply period includes: ； ； ； ； ； ； in, Is a binary variable, indicating the Are nodes divided into islands? ,like Indicates the Nodes are divided into islands ,like Indicates the Nodes not assigned to islands ; is the collection of all islands, and They are the important load set and the distributed generation unit set; Representation node belong and does not belong to the set ; Indicates an island with black start, frequency regulation and voltage regulation capabilities The connection status of the potential root node, if connected is 1, if disconnected is 0; Is a binary variable representing the line Is it included in the island? If so, ,otherwise ; It's a loop The number of lines included, is a collection of cycles, and is the number of branches in each loop.

4. The method according to claim 1, wherein In step Q4, solving and executing the emergency repair and restoration plan includes: establishing an emergency repair and restoration plan, including: establishing an upper model and a lower model to solve the emergency repair and restoration plan; wherein the upper model is used to plan the travel route of the maintenance team with the goal of minimizing the expected travel time and power outage loss in the transportation network; and the lower model is used to adjust resource scheduling with the goal of optimizing economic efficiency and maximizing the utilization of distributed power generation units.

5. The method according to claim 4, characterized in that The objective function of the upper model is: ； ； ； in, and are the expected travel times in the transportation network and power outage losses The weight coefficient of It is an emergency center The maintenance team, It's the maintenance team driving routes; is an edge in the transportation network topology; It's the edge expected travel time; is a set of time period numbers; Representation node In the time period Active power within; A set of fault point numbers; By the fault point The set of nodes that caused the power outage; is a node The load demand on is a node Priority weight of upper load; Represents a binary variable, representing the fault point In the time period Is it repaired? If the fault point In the time period If the internal has been repaired, the value is 1, otherwise it is 0; and are the weight coefficients of the power outage loss of all loads and the power outage loss caused by the fault point, respectively.

6. The method according to claim 4, characterized in that The objective function of the lower model is: ； ； ； ； ； in, and Economic objectives and the use of distributed generation units The weight coefficient of Distributed Generation Unit In the time period Internal efforts, Distributed Generation Unit Revenue function per unit output; Is connected to the node A collection of distributed generation units on 、 and is the weight coefficient, which is used to distinguish the importance of different loads; is a node Priority weight of upper load; By the fault point The set of nodes that are out of power due to It is the failure point The time when the repair task was completed; It is a set of nodes with three levels of transferable load; and are the end and start time periods of the acceptable transfer interval, respectively; is a node Unit dispatch cost of transferable load; and is a node In the time period Transferable load power before and after internal dispatch; Indicates the time period Distributed power generation units Unit output income; It is a collection of energy storage; Indicates time period Energy Storage Unit discharge power gain, It is energy storage In the time period Discharge power within It is a collection of electric vehicles; Indicates time period electric vehicles The unit discharge power gain, It's an electric car In the time period Discharge power within Indicates time period electric vehicles The unit charging power cost, It's an electric car In the time period Charging power within Indicates time period Energy Storage The unit charging power cost, It is energy storage In the time period Charging power within Shows the distribution network and transmission network in the time period The unit power interaction cost when The time period between the transmission network and the distribution network The interaction power within Indicates the distribution network and microgrid in the time period The unit power interaction cost within Is the time period between the microgrid and the distribution network Interaction power within; Is a binary variable representing the fault point In the time period Is it repaired? If the fault point In the time period Not repaired inside ,otherwise .

7. The method according to claim 6, characterized in that The constraints of the lower model include: Important load priority constraints: ； in, For nodes The minimum active power requirement; Distributed generation unit priority scheduling constraints: ； in, It is the priority scheduling coefficient, ranging from 0 to 1. The larger it is, the higher the scheduling priority. It is a distributed power generation unit Maximum capacity; Energy storage constraints: ； ； ； in, Represents energy storage In the time period The amount of electricity inside; For time period length; For energy storage In the time period Charging time within The proportion of For energy storage In the time period The discharge time within The proportion of , energy storage In the time period The total charge and discharge time is less than ; It is energy storage The maximum charging power, It is energy storage Maximum discharge power; Electric vehicle constraints: ； ； ； in, Representing electric vehicles In the time period The battery level at the start; For time period length; For electric vehicles In the time period Charging time within The proportion of For electric vehicles In the time period The discharge time within proportion; , electric vehicles In the time period The total charge and discharge time is less than ; It's an electric car The maximum charging power, It's an electric car Maximum discharge power; Interaction constraints between microgrid and distribution network: ； in, Represents the set of nodes in the microgrid; Represents the collection of distributed generation units within the microgrid; Representation node In the time period The load power within Indicates the time period between the microgrid and the distribution network Interaction power within; Interaction constraints between transmission and distribution networks: ； in, Indicates time period The maximum interactive power between the internal transmission grid and the distribution grid, Indicates time period Interaction power between the internal transmission grid and the distribution grid; Microgrid autonomous operation constraints: ； in, It is a microgrid Electric car collection inside, microgrids In the time period The amount of energy stored inside.

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