Post-disaster Cooperative Repair and Dispatch Method for Urban Distribution Network and Traffic System

Abstract

The present invention proposes a method for coordinated post-disaster repair and scheduling of urban distribution networks and transportation systems. The method comprises the following steps: obtaining data information based on faulty components of the urban distribution network and damaged / interrupted roads in the transportation system; establishing constraints for the post-disaster operation and scheduling of the urban distribution network; establishing constraints for the post-disaster operation and scheduling of the transportation system; establishing associated constraints for the repair decision-making constraints of the power system repair team subject to the road operation status; combining the respective repair decision constraints, establishing a coordinated post-disaster repair and scheduling optimization model for the urban distribution network and transportation system with the objective function of maximizing the total power load recovery and road traffic flow; and using the obtained data information as input parameters of the coordinated repair and scheduling optimization model to solve and obtain a repair scheduling plan. The present invention considers the impact of dynamic changes in the road status of the transportation system on the repair decision-making of the power system, improves the post-disaster recovery efficiency of the distribution network and transportation system, and reduces the economic losses caused by power outages.

Description

Technical Field

[0001] The present invention relates to the technical field of post-disaster emergency repair scheduling for power systems and transportation systems, and particularly to a method for collaborative emergency repair scheduling of urban distribution networks and transportation systems after disasters. Background Art

[0002] In recent years, the frequency and intensity of natural disasters such as floods and ice disasters caused by extreme weather events have gradually increased, posing a huge challenge to the safe and stable operation of power systems.

[0003] Compared with the transmission network, the urban distribution network is directly connected to users and lacks resilience, resulting in a larger fault range and more serious economic losses caused by extreme weather events. At the same time, the urban distribution network and the transportation system restrict and rely on each other. Extreme weather events such as floods and ice disasters can cause serious failures in both the urban power grid and the transportation system simultaneously. The interrupted traffic roads restrict the travel arrangements and repair efficiency of the power system repair teams. In this context, taking multi-system joint decision-making and collaborative actions can play an important role in quickly restoring power supply and road traffic and reducing social and economic losses.

[0004] The post-disaster recovery of power systems and transportation systems mainly relies on their respective manual repair teams. Managers need to determine the priority order of repairing faulty facilities and the task arrangements of different repair teams. Existing research has rarely considered the collaborative cooperation of post-disaster emergency repairs of power systems and transportation systems, and has not considered the dynamic changes in the traffic road conditions during the entire recovery process. Therefore, it is impossible to update the emergency repair decision of the power system in a timely manner to shorten the post-disaster recovery time. In addition, due to the long post-disaster recovery period, new faults may occur during the process, making it difficult for managers to formulate the scheduling decision of the repair team at one time immediately after the disaster occurs.

[0005] Therefore, in order to improve the post-disaster recovery efficiency, it is necessary to propose a collaborative emergency repair scheduling method for urban distribution networks and transportation systems, which adopts a sequential solution method to timely utilize post-disaster fault information and road condition information, reduce the computational scale of the scheduling decision problem, and avoid waste of resources of repair teams. Summary of the Invention

[0006] In response to the technical problem that existing post-disaster emergency repair scheduling methods do not consider the dynamic changes in traffic conditions throughout the recovery process, the present invention proposes a post-disaster collaborative emergency repair scheduling method for urban distribution networks and transportation systems. Taking into account the mutual relationship between the urban distribution network and the transportation system, the emergency repair scheduling of the power system and the transportation system are combined to maximize the restored power load and road traffic flow. Based on real-time fault information and network data, the collaborative emergency repair scheduling optimization model under the current step is solved to obtain the order in which the repair team repairs the faulty power components and damaged traffic roads. Then, in the next step, the emergency repair scheduling model is solved based on the latest fault information. The time width between each two steps is fixed. The present invention can obtain a practical and feasible post-disaster emergency repair scheduling plan for the distribution network and transportation system, quickly repair key power facilities and damaged roads after a natural disaster, and improve the efficiency of post-disaster recovery.

[0007] In order to achieve the above-mentioned object, the technical solution of the present invention is implemented as follows: a method for coordinated emergency repair and dispatching of urban distribution network and transportation system after a disaster, the steps of which are as follows:

[0008] Step S1: Based on current user feedback and monitoring information, determine the faulty components of the urban distribution network and damaged / interrupted roads in the transportation system after the natural disaster; obtain the topology and network parameters of the distribution network and transportation system, the amount of active load lost at the node, the real-time road traffic flow, and the traffic flow data stored at the parking lot;

[0009] Step S2: Considering the radial topology reconstruction operation constraints of the distribution network, the balance constraints of active power and reactive power of the nodes, and the upper and lower limit constraints of each physical variable, the constraints for the post-disaster operation and scheduling of the urban distribution network are established;

[0010] Step S3: Considering the transfer characteristics of traffic flow at nodes and roads in the transportation system, a linear traffic flow transmission equation is constructed based on the transferable load method to establish the constraints for post-disaster operation and scheduling of the transportation system;

[0011] Step S4: Using the navigation system to obtain the path of the distribution network repair vehicle from the departure point to each fault line, identify the roads in the transportation system that have been interrupted due to the disaster, and establish the repair decision-making constraints associated with the road operation status of the power system repair team; the constraints of the post-disaster operation and scheduling of the urban distribution network, the constraints of the post-disaster operation and scheduling of the transportation system, and the repair decision constraints of the urban distribution network and the transportation system, as well as the repair decision-making constraints associated constraints, are combined to maximize the total power load recovery and road traffic flow as the objective function, and establish an optimization model for the coordinated repair scheduling of the urban distribution network and the transportation system after the disaster;

[0012] Step S5: Use the data information obtained in Step S1 as the input parameters of the collaborative emergency repair scheduling optimization model, solve the collaborative emergency repair scheduling optimization model for the urban distribution network and transportation system after the disaster, and obtain the emergency repair scheduling plan at the current step;

[0013] Step S6: The emergency repair teams of the urban power grid and the transportation system implement collaborative emergency repair according to the emergency repair scheduling plan. After completing the emergency repair at the current step, return to Step S1. If there are newly added faulty components or damaged roads, substitute the fault data information into the collaborative emergency repair scheduling optimization model established in Step S4 and solve and execute it until all faulty components and damaged roads return to normal operation.

[0014] Preferably, the network parameters in Step S1 include the impedance of each power line in the distribution network, the maximum allowable active power carrying capacity, the maximum allowable traffic flow of each road in the transportation system, and the maximum allowable traffic flow of each parking location.

[0015] Preferably, the balance constraints of the active power and reactive power of the node are:

[0016]

[0017]

[0018] where s represents the current scheduling step number; is the active power of the generator connected to node i at the s-th step; represents the binary variable indicating whether node i is energized at the s-th step. If node i is energized, it is 1, otherwise it is 0; is the active load carried by node i; (i) represents the set of nodes connected to node i, is the active power flow on branch ij at the s-th step; is the reactive power of the generator connected to node i at the s-th step; is the reactive load carried by node i; is the reactive power flow on branch ij at the s-th step.

[0019] Preferably, the operating upper and lower limit constraints include:

[0020] The DistFlow linear power flow equation of the distribution network power flow model: For the active power flow of branch ij and the reactive power flow They and the terminal voltages of the two ends of the branch and The branch resistance r ij and the branch reactance x ij Satisfy the following relationship:

[0021]

[0022] In formula (3), M is a relatively large constant; A binary variable indicating whether branch ij is energized and operating at the s-th step. If there is power flow on this branch, i.e., it is energized, it is 1; otherwise, it is 0;

[0023] The maximum allowable active power flow constraint for the branch is:

[0024] Node voltage The physical operation constraint of is:

[0025] The active power output constraint of the generator is:

[0026] The reactive power output constraint of the generator is:

[0027] Among them, is the maximum active power that line ij can allow; V i max and V i min are respectively the upper and lower limits of the allowable node voltage of node i; and are respectively the upper and lower limits of the active power output / reactive power output of the generator connected to node i.

[0028] Preferably, the radial topological reconstruction operation constraints of the distribution network include:

[0029] 1) The urban distribution network is a radial network. During the post-disaster recovery process, the urban distribution network needs to maintain radial operation. The constraints related to whether the downstream branch can be powered and the power supply status of its connected upstream branch include:

[0030] The constraint that branch ij can be energized and operating only when at least one of its upstream branches is powered:

[0031]

[0032] The radial network structure constraint derived based on graph theory:

[0033]

[0034] Among them, represents the energized state of an upstream branch hi of branch ij at the s-th step. h is any node in the set of nodes connected to node i. If it is energized and operating, it is 1; otherwise, it is 0; N line is the number of branches in the urban distribution network; N bus is the number of nodes, Nsource is the number of power sources in the distribution network;

[0035] 2) During the post-disaster recovery process, once a faulty node or branch is restored to power, the constraints for it to remain powered in subsequent scheduling steps include:

[0036]

[0037]

[0038] Among them, is the energized state of node i at the (s + 1)-th step, is the energized operation state of branch ij at the (s + 1)-th step.

[0039] Preferably, the construction method of the linear traffic flow transmission equation is:

[0040] The relationship between the traffic flow on the node and the traffic flow on the road is:

[0041]

[0042]

[0043] Among them, respectively represent the traffic flow of node o at the s-th and (s - 1)-th steps of scheduling, represents the traffic flow transferred to node o at the s-th step, represents the traffic flow transferred out from node o at the s-th step; respectively represent the traffic flow of road od at the s-th and (s - 1)-th steps of scheduling, represents the traffic flow transferred to road od at the s-th step, represents the traffic flow transferred out from road od at the s-th step;

[0044] The traffic flow on the road is bidirectional or unidirectional. The relationship between the traffic flow transferred in / out on the node and the head / tail, forward / backward traffic flow on the road is:

[0045]

[0046]

[0047]

[0048]

[0049] Among them, represents the forward traffic flow at the head end of road od, represents the forward traffic flow at the tail end of road od; Indicates the reverse traffic flow at the beginning of road od, Indicates the reverse traffic flow at the end of road od;

[0050] For the entire traffic system, the traffic flow transferred into all locations and roads at the current moment is equal to the traffic flow transferred out, which is expressed as:

[0051]

[0052] Among them, N tra Indicates the number of nodes in the traffic system, N road Indicates the number of branch roads in the traffic system.

[0053] Preferably, the constraint conditions for the post-disaster operation scheduling of the traffic system are:

[0054] The traffic flow that can be accommodated at the parking locations (i.e., nodes) and on the roads (i.e., branch roads) of the traffic system has an upper limit and a lower limit of 0, and the constraint is expressed as:

[0055]

[0056]

[0057] Among them, Is the maximum traffic flow that can be accommodated at traffic system node o; Is the binary variable of the traffic state of traffic system branch road od at the s-th step. If it is 1, it means the road is unobstructed, otherwise it is 0; Indicates the maximum traffic flow that can be accommodated on road od at the s-th step;

[0058] The constraint conditions for the traffic flow transferred out or in on the road are:

[0059]

[0060]

[0061] The constraint conditions for the traffic flow transferred in or out on the road are:

[0062]

[0063]

[0064] Preferably, the repair decision constraints of the urban distribution network and the traffic system respectively include

[0065] The constraint conditions for the power lines without faults and the traffic roads without damage are:

[0066]

[0067]

[0068] Among them, the binary emergency repair decision variable and When the emergency repair team c of the distribution network repairs the power branch ij at the s-th step, the emergency repair decision variable is 1, otherwise it is 0; when the emergency repair team z of the transportation system repairs the interrupted road od at the s-th step, the emergency repair decision variable is 1, otherwise it is 0; P normal represents the set of power lines without faults, and F normal represents the set of transportation roads that are not damaged and interrupted;

[0069] For the constraint condition on whether the faulty line and the damaged road can return to normal operation depending on whether they are repaired:

[0070]

[0071]

[0072] Among them, represents the binary variable indicating whether the branch ij is energized at the s-th step; is the binary variable of the traffic state of the branch od of the transportation system at the s-th step;

[0073] Constraint condition that the number of lines and roads that the emergency repair teams of the power system and the transportation system can repair within each scheduling interval is limited:

[0074]

[0075]

[0076] Among them, N c is the number of lines that the emergency repair team of the power system can repair within a scheduling interval, and P damage is the set of faulty lines of the power system; N z is the number of roads that the emergency repair team of the transportation system can repair within a scheduling interval, and F damage is the set of interrupted roads of the transportation system;

[0077] The constraint related to the emergency repair decision of the power system emergency repair team restricted by the road operation state is:

[0078]

[0079] Among them, is a binary variable, which is 1 when there is a passable path f to ensure that the power system emergency repair team c departs from the station to the faulty line ij at the s-th step, otherwise it is 0;

[0080] When all the roads included in any path f are in a passable state, the repair team of the distribution network can carry out repair operations. This dependency relationship can be expressed based on an improved Boolean logic expression. Suppose there are two paths f1 and f2, and the implementation of the repair operation can be ensured as long as any one of them is passable. The roads included in path f1 are ox, dy, and dz, and the roads included in path f2 are on and dm. At this time, formula (31) is further expressed in detail as follows:

[0081]

[0082] Suppose the roads ox, dy, and on are all in a normal passable state, and their state variables take the value of 1. Therefore, formula (32) is further simplified in practice as follows: It is used to indicate that whether the repair decision can be executed only depends on whether the passable path contains a faulty road.

[0083] Preferably, the objective function is as follows:

[0084]

[0085] Among them, w is the weight coefficient of the power system, N bus is the number of nodes in the power system, N road is the number of roads in the transportation system;

[0086] The collaborative repair scheduling optimization model for the urban distribution network and transportation system after the disaster is as follows:

[0087] Objective function: formula (33); Constraint conditions: formulas (1)-(31).

[0088] Preferably, the collaborative repair scheduling optimization model is a mixed-integer linear programming model, and is solved by using a commercial optimization solver such as Gurobi or Mosek.

[0089] Compared with the prior art, the present invention has at least the following beneficial effects:

[0090] 1. In the post-disaster repair and restoration of the power system, the dynamic changes of the traffic road conditions and their impacts on the scheduling decision of the repair vehicle team are considered, and a practical and efficient component repair plan can be obtained.

[0091] 2. By using the characteristics of traffic flow transfer in the transportation system, a linear traffic flow transmission model is established. Compared with the traditional semi-dynamic traffic flow and cellular automaton traffic flow modeling methods, it has the advantages of linearization, easy understanding, and high computational efficiency, and is more suitable for the field of post-disaster repair optimization.

[0092] In summary, the present invention establishes an optimization model for post-disaster collaborative repair and dispatch of urban distribution networks and transportation systems, and uses commercial optimization to solve the post-disaster repair decision-making problems of the power system and the transportation system, so as to restore the power load with interrupted power supply and the traffic roads with interrupted traffic as soon as possible, and improve the emergency management level of the city. The present invention takes into account the impact of the dynamic changes in the road conditions of the transportation system on the repair decision-making of the power system, adopts the method of collaborative repair, improves the post-disaster recovery efficiency of the power grid and the transportation system, reduces the economic losses caused by power curtailment, and weakens the impact of road interruption on daily travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0094] Figure 1 It is a schematic diagram of the traffic network nodes and branch traffic flows of the present invention.

[0095] Figure 2 It is a schematic diagram of the traffic flow modeling of the road traffic network of the present invention.

[0096] Figure 3 It is the solution framework of the post-disaster collaborative repair and dispatch model of the present invention.

[0097] Figure 4 It is a simplified diagram of the IEEE-33 node distribution network and the 12-node traffic network.

[0098] Figure 5 It is the post-disaster recovery result diagram of the load of the IEEE-33 node distribution network.

[0099] Figure 6 It is a simplified diagram of the IEEE-136 node distribution network and the 20-node traffic network.

[0100] Figure 7 It is the post-disaster recovery result diagram of the load of the IEEE-136 node distribution network. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0101] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0102] AsFigure 1 As shown in Figure 1 , a method for collaborative emergency repair scheduling of urban distribution network and transportation system after disaster includes the following steps:

[0103] Step S1: According to the current user feedback and monitoring system information, determine the faulty components of the urban distribution network and the damaged / interrupted roads of the transportation system after natural disasters; obtain data information such as the topologies of the distribution network and transportation network, network parameters, the amount of active load of lost nodes, real-time road traffic flow, traffic flow stored at parking locations, etc.

[0104] The network parameters include the impedance of each power line, the maximum allowable active power carrying capacity, the maximum allowable traffic flow of each road, the maximum allowable traffic flow of each parking location, etc. The nodes of the transportation system are each parking location, and the branches are roads; the nodes of the distribution network are buses, and the branches are power lines.

[0105] Step S2: Considering the operation constraints of the radial topology reconstruction of the power grid, the balance constraints of the active power and reactive power of nodes, and the upper and lower limits of the operation of each physical variable, establish an operation scheduling model for the urban distribution network after disaster. [[ID=XXX]] [[ID=XXX]]

[0106] Specifically, the power supply-demand balance equation of the urban distribution network in Step S2 is:

[0107]

[0108]

[0109] In formula (1), s represents the current scheduling step number; is the active power output of the generator connected to node i at the s-th step; is a binary variable indicating whether node i is energized at the s-th step. If the node is energized, it is 1, otherwise it is 0; is the active load borne by node i; (i) represents the set of nodes connected to node i, is the active power flow on branch ij at the s-th step. Similarly, in formula (2), is the reactive power output of the generator connected to node i at the s-th step; is the reactive load borne by node i; is the reactive power flow on branch ij at the s-th step.

[0110] Formula (1) indicates that the active power injected into the node minus the active load borne by the node is equal to the sum of the active power flows of all branches connected to the node. It represents the active power balance equation of the distribution network nodes, and formula (2) is the reactive power balance equation of the distribution network nodes.

[0111] For the active power flow and reactive power flow They and the terminal voltages of the nodes at both ends of the branch and the branch resistance r ij and the branch reactance x ij satisfy the following relationship:

[0112]

[0113] In Equation (3), M is a relatively large constant, and its value should not be too large. It only needs to make Equation (3) hold. Therefore, it can be set according to the value ranges of the voltage variables and power variables in Equation (3); is a binary variable indicating whether the branch ij is energized and operating at the s-th step. If there is a power flow on the branch, that is, it is energized, then it is 1; otherwise, it is 0. This equation is the DistFlow linear power flow equation widely used in the power grid flow model.

[0114]

[0115]

[0116]

[0117]

[0118] Among them, is the maximum active power that the line ij can allow; V i max and V i min are respectively the upper and lower limits of the allowable node i voltage; and are respectively the upper and lower limits of the active / reactive power output of the generator connected to node i.

[0119] Equation (4) represents the maximum allowable active power flow constraint of the branch, Equation (5) represents the physical operation constraint of the node voltage , and Equations (6) and (7) respectively represent the limit constraints on the active power output and reactive power output of the generator.

[0120] Urban distribution networks are generally radial networks. During the post-disaster recovery process, it is necessary to maintain radial operation. Therefore, whether the downstream branches can be powered is related to the power supply status of the upstream branches connected to them. The corresponding grid radial topological structure constraint conditions are as follows:

[0121]

[0122]

[0123] Among them, Indicates the energized state of an upstream branch hi of branch ij. h is any node in the set of nodes connected to node i. It is 1 if energized and 0 otherwise. Equation (8) ensures that in a radial network, branch ij can be energized if and only if at least one of its upstream branches is powered. N line is the number of branches in the urban distribution network; N bus is the number of nodes, N source is the number of power sources in the distribution network. Equation (9) is the radial network structure constraint derived from graph theory, that is, for a radial network, the number of branches is equal to the number of nodes minus the number of power sources (i.e., root nodes).

[0124] During the post-disaster recovery process, once a faulty node or branch is restored to power, it remains powered in subsequent scheduling steps, which are specifically described as follows:

[0125]

[0126]

[0127] where is the energized state of node i at step s + 1, is the energized operating state of branch ij at step s + 1.

[0128] Step S3: Considering the transfer characteristics of traffic flow in the traffic system among nodes and roads, based on the transferable load method, construct a linear traffic flow transmission equation and establish a post-disaster operation scheduling model for the traffic system.

[0129] Specifically, the method for establishing the post-disaster operation scheduling model of the traffic system in step S3 is as follows:

[0130] First, each origin or destination of the traffic system can be abstracted as a node in the traffic network, and each road can be abstracted as a branch in the traffic network. The traffic flow on the road is generally quantified by the number of vehicles per unit time, with vehicles per hour as the mathematical unit. Different from the active power flow of the power system, the traffic flow only transfers among different branches and nodes and there is no consumption or production process in the short term. Therefore, the traffic flow is similar to the transferable load in the power system. Second, if some roads are affected by natural disasters and interrupted or blocked, the traffic flow on these roads is only transferred to other locations or roads without loss, which is also different from the characteristic of load reduction caused by faults in some nodes and branches of the power system after natural disasters. Based on these characteristics, the present invention proposes a linear traffic flow transmission modeling method, and the specific method is as follows:

[0131] Within each scheduling time domain interval, traffic flow transfers on roads or nodes, and the traffic flow transmission of the traffic system can be described by using the traffic flow on roads or nodes. As Figure 1 shown, let represent the traffic flow of node o at the s-th step of scheduling, represent the traffic flow transferred to node o at the s-th step, represent the traffic flow transferred out from node o at the s-th step; represent the traffic flow of road od at the s-th step of scheduling, represent the traffic flow transferred to road od at the s-th step, represent the traffic flow transferred out from road od at the s-th step. The relationship between the traffic flow on nodes and the traffic flow on roads is expressed as follows:

[0132]

[0133]

[0134] Equation (12) indicates that the traffic flow of node o at the current scheduling step s is equal to the traffic flow f o s-1 of this node at the previous step plus the currently transferred traffic flow minus the currently transferred-out traffic flow Similarly, Equation (13) indicates that the traffic flow of node o at the current scheduling step s is the sum of the traffic flow of this node at the previous step and the currently transferred traffic flow minus the currently transferred-out traffic flow.

[0135] In practice, the traffic flow on roads can be two-way or one-way. As Figure 2 shown, let represent the forward traffic flow at the head end of road od, represent the forward traffic flow at the tail end of road od; represent the reverse traffic flow at the head end of road od, represent the reverse traffic flow at the tail end of road od. Then, the traffic flow transferred in / out on nodes and the head / tail end, forward / reverse traffic flow on roads have the following relationships:

[0136]

[0137]

[0138]

[0139]

[0140] Equations (14) and (15) represent the equality relationship between the incoming traffic flow at node o. Equations (16) and (17) respectively represent the equality relationship between the incoming and outgoing traffic flows on road od and the head / tail, forward / backward traffic flows on the road.

[0141] For the entire traffic system, the incoming traffic flow at all locations and roads at the current moment is equal to the outgoing traffic flow, which is expressed as follows:

[0142]

[0143] In Equation (18), N tra represents the number of nodes in the traffic system, and N road represents the number of branches in the traffic system. This equation reflects the real-time balance of traffic flow.

[0144] The traffic flow that can be accommodated at the parking locations (i.e., nodes) and roads (i.e., branches) of the traffic system has an upper limit, and the lower limit is 0, which is expressed as follows:

[0145]

[0146]

[0147] In Equation (19), is the maximum traffic flow that can be accommodated at node o of the traffic system; is a binary variable representing the traffic status of branch od in the traffic system. If it is 1, it means the road is unobstructed, otherwise it is 0; represents the maximum traffic flow that can be accommodated on road od. Correspondingly, the traffic flow that can be transferred in or out on the road has the following restrictions:

[0148]

[0149]

[0150] Equation (21) means that the maximum traffic flow that can be transferred into location o at scheduling step s does not exceed the maximum capacity of this location minus the traffic flow that already exists at this location in the previous step s - 1 Equation (22) means that the maximum traffic flow that can be transferred out from location o currently does not exceed the traffic flow that already exists at the previous moment. Similar to locations, the traffic flow that can be transferred in or out on the road meets the following restrictions:

[0151]

[0152]

[0153] Step S4: Use the navigation system to obtain the route of the power system repair vehicle from the departure point to each fault line, identify the transportation system roads that have been interrupted due to the disaster, and establish the repair decision-making constraints associated with the power system repair team's road operating status. Combine the post-disaster operation and scheduling model of the urban distribution network and the post-disaster operation and scheduling model of the transportation system, the repair decision-making constraints associated with the repair decision-making constraints, and the repair decision constraints. With maximizing the total power load recovery and road traffic flow as the objective function, an optimization model for the coordinated post-disaster repair scheduling of the urban distribution network and transportation system is established.

[0154] Specifically, in step S4, the emergency repair decision constraints of the urban distribution network and the transportation system are:

[0155] First, define the binary repair decision variable and When the distribution network repair team c repairs the power branch ij, the repair decision variable is 1, otherwise it is 0; when the traffic system repair team z repairs the interrupted road od, the repair decision variable is 1, otherwise it is 0. For power lines that have not failed and traffic roads that have not been damaged, there are the following constraints:

[0156]

[0157]

[0158] In formula (25), P normal represents the set of power lines without faults. In formula (26), F normal represents the set of traffic roads that are not damaged and interrupted. Equations (25) and (26) can ensure that non-faulty components are fixed parameters rather than decision variables and do not participate in the repair decision.

[0159] For faulty lines and damaged roads, whether they can be restored to normal operation depends on whether they are repaired:

[0160]

[0161]

[0162] Equation (27) indicates that when at least one repair team in the power system repairs the faulty power line ij, the line can be connected to the main grid and return to normal, but it does not mean that it will be powered immediately after repair. According to the operating characteristics of the distribution network, whether the line can be powered depends on whether its upstream branch is powered. Therefore, by combining Equation (8) and Equation (27), it can be seen that whether a faulty power line can be restored to power depends on whether it is repaired and whether its upstream is restored to power. Similarly, Equation (28) indicates that when at least one repair team in the transportation system repairs the interrupted road od, the road can return to normal traffic.

[0163] The number of lines and roads that the repair teams in the power system and the transportation system can repair within each scheduling interval is limited:

[0164]

[0165]

[0166] In Equation (29), N c is the number of lines that the power system repair team can repair within a scheduling interval, and P damage is the set of faulty power lines in the power system. In Equation (30), N z is the number of roads that the transportation system repair team can repair within a scheduling interval, and F damage is the set of interrupted roads in the transportation system.

[0167] Due to the geographical space coupling between the urban distribution network and the transportation system, when natural disasters occur, not only the distribution network but also the transportation system will be damaged. When repairing the faulty components of the distribution network, the repair personnel take the repair vehicle and depart from a certain station. The roads along the way may be impassable due to the disaster. At this time, the destination can only be changed, that is, the order of the repaired components is changed. Therefore, the scheduling decision of the power system repair team is restricted by the traffic path status. Correspondingly, the associated constraints of the repair decision are expressed as follows:

[0168]

[0169] In Equation (31), is a binary variable, which is 1 when there is a passable path f to ensure that the power system repair team c departs from the station to the faulty line ij, and 0 otherwise. Equation (31) indicates that when at least one passable path exists, the power repair can be completed. This reflects the associated constraints and other constraint conditions of the repair teams in the two systems.

[0170] When all the roads included in any path f are in a passable state, the emergency repair team of the distribution network can carry out emergency repair actions, and this dependency relationship can be expressed based on an improved Boolean logic expression. For example, assume there are two paths f1 and f2, and the implementation of emergency repair actions can be ensured as long as any one of them is passable. The roads included in path f1 are ox, dy, and dz, and the roads included in path f2 are on and dm. At this time, Equation (31) can be further elaborated as follows:

[0171]

[0172] Assume that the roads ox, dy, and on are all in normal passable states, and their state variables take the value of 1. Therefore, Equation (32) can be further simplified in practice as It is used to indicate that whether the emergency repair decision can be executed only depends on whether the passable path contains a faulty road.

[0173] Therefore, the objective function of the collaborative emergency repair scheduling model is:

[0174]

[0175] In Equation (33), w is the weight coefficient of the power system. Since the unit of power load is generally MW / hour and the unit of traffic flow is generally the number of vehicles / hour, the total power load of the distribution network is often much smaller than the total road traffic flow numerically. In order to make the emergency repair of power load account for a higher proportion, according to the numerical values of the actual system, w generally takes [1000, 10000]. N bus is the number of nodes in the power system, and N road is the number of roads in the traffic system. The first term of Equation (33) represents the total active power load borne by the distribution network at the current step s. The larger its value, the more total load is restored. The second term represents the total traffic flow on the roads in the traffic system at the current step s. The larger its value, the fewer roads with interrupted traffic. Since the unit of traffic flow is vehicles / hour and its actual value is relatively large, and the active power load of the distribution network may be dozens to hundreds of megawatts, when using megawatts and vehicles / hour as units, the first term of Equation (33) may have too small a value to occupy a large weight in the optimization model, resulting in the optimization decision giving priority to restoring the traffic system completely. Therefore, the weight coefficient w is increased to strengthen the priority of post-disaster emergency repair of the power system.

[0176] Step S5: Use the data information obtained in Step S1 as the input parameters of the scheduling optimization model, and use commercial software such as Mosek and Gurobi to solve the established mixed-integer linear programming model to obtain the emergency repair scheduling plan at the current step.

[0177] The overall post-disaster collaborative emergency repair scheduling model can be described as follows: objective function: Equation (33); constraint conditions: Equations (1)-(31). This model is a mixed-integer linear programming model and can be conveniently solved using commercial optimization solvers such as Gurobi and Mosek. The sequential solution method is adopted to solve the scheduling plan at the current step. The sequential solution method means that at the current step s, based on the currently known fault information, the scheduling model is solved to obtain the emergency repair plan at step s. After the emergency repair is implemented, the scheduling step s = s + 1, and then it is solved again. The sequential solution method is different from the traditional one-time solution. It can timely use the latest fault information to make emergency repair decisions. The computational effort at each step is small, but it needs to be solved multiple times. The traditional non-sequential one-time solution method is to find the emergency repair scheduling plan for all times at once, and it can only use the currently known information. Once the fault information is updated, the formulated scheduling plan is no longer applicable.

[0178] The post-disaster collaborative emergency repair scheduling solution framework is as Figure 3 shown. Input the fault information, network parameters of the transportation system and the distribution network, update the information of the fault lines and terminal roads, solve the post-disaster collaborative emergency repair scheduling model at step s, issue the emergency repair decision, and each emergency repair team executes the emergency repair task according to the decision, and judge whether all the damaged components and interrupted roads have returned to normal.

[0179] Step S6: The urban power grid emergency repair team and the transportation system emergency repair team implement collaborative emergency repair according to the emergency repair scheduling plan. After completing the emergency repair at the current step, return to Step S1. If there are newly added fault components or damaged roads, substitute the fault information into the collaborative emergency repair scheduling model established in Step S4 and solve and execute it until all the fault components and damaged roads return to normal operation.

[0180] Considering the emergency repair efficiency of the distribution network and the transportation system, it is assumed that the time interval between each scheduling step s and s + 1 is 3 hours. The emergency repair team of the distribution network needs to depart from the station to the fault line. It is assumed that 1 fault line can be repaired within every 3 hours. The emergency repair and restoration of the transportation roads may last from several hours to several days. However, to reflect the impact of the transportation road status on the emergency repair of the power system, it is assumed that the emergency repair team of the transportation system can restore 1 damaged road within 3 hours. To measure the benefits of the method proposed in the present invention, 3 comparative examples are designed:

[0181] Example 1: After the disaster, the urban distribution network and the transportation system are repaired independently. During the process of formulating the repair decision by the power grid management personnel, the road conditions of the transportation system are not considered. Specifically, for the distribution network, its repair decision model is as follows: Objective function: the first term of Equation (34), that is, the power load recovery amount; Constraints: Equations (1)-(11), (25), (27), (29). For the transportation system, its repair decision model is as follows: Objective function: the second term of Equation (34); Constraints: (12)-(24), (26), (28), (30).

[0182] Example 2: After the disaster, the urban distribution network and the transportation system are repaired independently. However, during the repair decision process of the distribution network, the road conditions of the transportation system are considered and used as known parameters in the repair scheduling model, that is, in Equation (33), is a known parameter and not a binary variable.

[0183] Example 3: The collaborative repair scheduling of the urban distribution network and the transportation system after the disaster proposed by the present invention.

[0184] The optimization models of the three examples are all built based on the Yalmip toolbox in the Matlab / Simulink software platform and solved by Mosek. The effects of the present invention will be described below in combination with two specific embodiments.

[0185] Embodiment 1:

[0186] Based on the data of the IEEE 33-node distribution network and the 12-node transportation system, simulation experiments of the three examples are carried out. The detailed data of the 33-node distribution network are obtained from the Matpower toolbox, and the total pre-disaster active power load is 3.715 megawatts per hour. The 12-node transportation network contains 20 roads, and the total pre-disaster traffic flow on the roads is 11,470 vehicles per hour. Numerically speaking, the total traffic flow of 11,470 on the roads is much larger than the power load of 3.715. Therefore, in order to give a higher priority to the power load recovery in the collaborative recovery after the disaster, the weight coefficient w in Equation (34) of Example 3 is set to 10,000.

[0187] As Figure 4As shown in the figure, assume that after an extreme event occurs, 11 power grid lines, namely 12-13 (corresponding number is 12, the same below), 13-14 (13), 14-15 (14), 17-18 (17), 20-21 (20), 21-22 (21), 23-24 (23), 24-25 (24), 29-30 (29), 30-31 (30), 32-33 (32), experience power outages due to faults. The power load that is not interrupted at the initial moment after the disaster is 1.625 MW. In the transportation system, 4 roads, namely 4-8, 7-8, 7-11, and 8-11, are damaged and the traffic is interrupted. The traffic flow on these 4 roads at the initial moment of post-disaster recovery (i.e., s = 0) is 0, which means that the vehicles on these 4 roads are transferred to other locations after the disaster. The repair team of the power grid starts from location 4 of the transportation system. Considering the geographical correlation between the power grid and the transportation system, assume that the repair of power line 12 is restricted by the traffic condition of road 8-11, and the repair of power line 29 is restricted by the traffic condition of road 4-8.

[0188] Assume that there are 3 repair teams in the power grid and 1 repair team in the transportation network. This means that 3 fault lines and 1 interrupted road will be repaired in each scheduling interval. Therefore, for the power grid with 11 line faults, it takes a total of 12 hours to complete the repair in 4 steps. For the 4 interrupted roads in the transportation network, all are repaired after 4 steps as well. The simulation experiment results of the three examples are shown in Tables 1 and 2.

[0189] It can be seen from Table 1 that there are significant differences in the repair sequences of power components obtained from the 3 examples. In order to restore more power load, the repair teams in Example 1 will first restore lines 23, 24, and 29 at s = 1, thereby restoring the active power loads at nodes 24, 25, and 30. However, in reality, the repair of power lines requires the repair team's vehicles to have accessible traffic paths. It is known that the repair of line 29 is restricted by the traffic condition of road 4-8, and the repair of line 12 is restricted by the traffic condition of road 8-11. Only when these two roads are in normal traffic can the power system repair team repair lines 12 and 29. As shown in Table 2, the repair team is dispatched to repair road 4-8 at s = 2, and road 8-11 is repaired by the repair team at s = 3. Therefore, in reality, line 29 can only be repaired after s = 2 (i.e., 6 hours after the disaster), and similarly, line 12 can only be repaired after s = 3 (i.e., 9 hours after the disaster). Thus, it can be seen that the repair decision of power components obtained from Example 1, which completely ignores the traffic conditions of roads, does not conform to reality.

[0190] As can be seen from Table 2, when the post-disaster collaborative emergency repair scheduling method proposed in the present invention (i.e., Example 3) is adopted, compared with Example 1 and Example 2, the emergency repair order of Road 4-8 is more advanced, and the emergency repair of Road 8-11 is still arranged after that of Road 7-11. This is because Road 7-11 is an important traffic road with a relatively high upper limit of traffic flow it can carry. If Road 8-11 is repaired to complete the emergency repair of Power Line 12, only the active load at Distribution Network Node 13 can be restored, and the active load at Node 13 is relatively low. Therefore, in Example 3, the obtained emergency repair decision advances the emergency repair order of Road 4-8 and has no impact on the emergency repair order of Road 8-11.

[0191] Table 1. Repair Sequence of Faulty Branches in IEEE-33 Node Distribution Network

[0192]

[0193] Table 2. Repair Sequence of Damaged Roads in 12-Node Traffic Network

[0194]

[0195] The restoration results of the post-disaster power load of the IEEE-33 node distribution network are as shown in the appendix Figure 5 As can be seen, in Example 1, the largest amount of power load is restored in the last few hours after the disaster, but the obtained emergency repair decision in Example 1 cannot be actually applied. Example 2 takes into account the traffic conditions of the roads, and the obtained emergency repair decision is feasible. However, the distribution network and the traffic system are repaired independently, resulting in the lowest power load restoration efficiency. In contrast, Example 3 can better balance the load restoration efficiency and obtain a practical emergency repair plan. Figure 5

[0196] Example 2:

[0197] The IEEE-136 node distribution network is a real power network in a certain city in Brazil. The relevant data can be obtained from the Matpower toolbox. The total pre-disaster power load is 18.3138 megawatts per hour. The 20-node traffic system has 40 roads, and the total traffic flow on the roads during normal pre-disaster operation is 64,200 vehicles per hour. Similarly, due to the large difference in the values of the power load and the road traffic flow, the weight coefficient w in Example 3 is still taken as 10,000.

[0198] As Figure 6As shown in the figure, assume that after an extreme event occurs, 34 roads in the distribution network fail, with numbers 5, 6, 8, 12, 13, 14, 23, 26, 28, 33, 35, 44, 49, 50, 55, 58, 59, 62, 70, 72, 79, 80, 82, 90, 92, 95, 102, 105, 111, 116, 124, 131, 133, 134. The power load that is not interrupted at the initial moment after the disaster is 6.5488 megawatts per hour. 10 roads in the transportation system are damaged and interrupted, namely roads 4, 8, 13, 14, 16, 21, 23, 28, 31, 32. The repair teams of the power system need to depart from location 6 of the transportation network to each faulty line. Due to the geographical coupling of the two systems, assume that the repair of power lines 33 and 35 is restricted by the traffic condition of road 32, the repair of lines 131, 133, 134 is restricted by the traffic condition of road 16, the repair of line 105 is restricted by the traffic condition of road 28, and the repair of lines 23, 26, 28 is restricted by the traffic condition of road 21.

[0199] Assume that there are 3 repair teams in the distribution network and 1 repair team in the transportation system, that is, 3 faulty lines and 1 interrupted road can be repaired in each scheduling step s. Therefore, the distribution network needs 12 steps, that is, 36 hours to fully recover, and the transportation system needs 10 steps, a total of 30 hours to fully recover. The simulation experiment results of the three examples are shown in Tables 3 and 4.

[0200] Table 3. Repair sequence of faulty branches in the 136-node distribution network

[0201]

[0202] Table 4. Repair sequence of damaged roads in the 20-node transportation network

[0203]

[0204] As shown in Table 3, before s = 2, the emergency repair decisions for power components in Example 3 and Example 2 are the same. This is because at this time, the damaged roads 16 and 25 in the two examples have not been repaired, resulting in the inability to repair power lines 131 and 105. The power system can only formulate emergency repair decisions based on the state parameters of impassable roads. Road 16 in Example 2 was repaired at s = 5, that is, the road could be normally passable 15 hours after the disaster. At this time, lines 131, 133, and 134 could be repaired at s = 6. Although Road 32 was repaired at s = 2, the emergency repair team of the power system in Example 2 did not choose to immediately repair lines 33 and 35 because the active power loads at the corresponding nodes 34 and 36 were relatively small. The emergency repair team preferred to repair other lines to restore more loads at each step. From Example 3 in Table 4, it can be seen that all roads restricting the emergency repair of power lines returned to normal after s = 5. Therefore, after s = 6, the emergency repair decision of the power system in Example 3 is the same as that in Example 1, indicating that power emergency repair is no longer restricted by the traffic road state thereafter.

[0205] The restoration results of the post-disaster power load of the IEEE-136-node distribution network are shown in the appendix Figure 7 as follows. It can be seen from this figure that at s = 1, Example 1's choice to repair power lines 70, 105, and 131 can restore significantly more active power load than Example 2 and Example 3. Due to considering the restriction of the traffic road's passing state, the load restoration efficiency of Example 2 is significantly reduced. The load restoration efficiency of Example 3 is higher than that of Example 2 but lower than that of Example 1, indicating that the collaborative emergency repair scheduling method proposed in the present invention can balance the emergency repair efficiency and the feasibility of the plan.

[0206] The present invention includes the following steps: determining the faulty components of the urban distribution network and the interrupted roads of the traffic system after a natural disaster, and obtaining information such as the power grid topology and network parameters, the load of each node, the traffic network topology and network parameters, etc.; based on the transferable load method, establishing a linear traffic flow transmission model for the traffic system; taking the maximization of the load restoration amount and the road traffic flow as the objective function, and considering constraints such as the radial topology reconstruction of the power grid, the power supply and demand balance, the real-time balance of traffic flow, and the correlation constraints of the emergency repair teams of the two systems, establishing a collaborative emergency repair scheduling optimization model; according to the currently known information, as the input parameters of the scheduling optimization model, using the sequential solution method to solve the scheduling plan at the current step; the emergency repair teams of the urban power grid and the traffic system implement collaborative emergency repair according to the emergency repair decision until all faulty components and damaged roads return to normal operation. The present invention considers the impact of the dynamic change of the traffic system road state on the power system emergency repair decision, adopts the way of collaborative emergency repair, improves the post-disaster restoration efficiency of the power grid and the traffic system, reduces the economic loss caused by power curtailment, and weakens the impact of road interruption on daily travel.

[0207] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A post-disaster collaborative repair and dispatch method for urban distribution network and transportation system, characterized in that, The steps are as follows: Step S1: Based on current user feedback and monitoring information, determine the faulty components of the urban distribution network and damaged / interrupted roads in the transportation system after the natural disaster; obtain the topology and network parameters of the distribution network and transportation system, the amount of active load lost at the node, the real-time road traffic flow, and the traffic flow data stored at the parking lot; Step S2: Considering the radial topology reconstruction operation constraints of the distribution network, the balance constraints of active power and reactive power of the nodes, and the upper and lower limit constraints of each physical variable, the constraints for the post-disaster operation and scheduling of the urban distribution network are established; Step S3: Considering the transfer characteristics of traffic flow at nodes and roads in the transportation system, a linear traffic flow transmission equation is constructed based on the transferable load method to establish the constraints for post-disaster operation and scheduling of the transportation system; The construction method of the linear traffic flow transmission equation is: The relationship between traffic flow on nodes and traffic flow on roads is: Among them, respectively represent the traffic flow of node o at the s-th and (s - 1)-th steps of scheduling, represents the traffic flow transferred to node o at the s-th step, represents the traffic flow transferred out of node o at the s-th step; respectively represent the traffic flow of road od at the s-th and (s - 1)-th steps of scheduling, represents the traffic flow transferred to road od at the s-th step, represents the traffic flow transferred out of road od at the s-th step; Traffic flow on a road is bidirectional or unidirectional. The relationship between the incoming / outgoing traffic flow at a node and the beginning / end, forward / reverse traffic flow on the road is: Among them, represents the forward traffic flow at the beginning of road od, represents the forward traffic flow at the end of road od; represents the reverse traffic flow at the beginning of road od, represents the reverse traffic flow at the end of road od; For the entire traffic system, the incoming traffic flow at all locations and roads at the current moment is equal to the outgoing traffic flow, which can be expressed as: Among them, N tra represents the number of nodes in the transportation system, and N road represents the number of branches in the transportation system; Step S4: Using the navigation system to obtain the path of the distribution network repair vehicle from the departure point to each fault line, identify the roads in the transportation system that have been interrupted due to the disaster, and establish the repair decision-making constraints associated with the road operation status of the power system repair team; the constraints of the post-disaster operation and scheduling of the urban distribution network, the constraints of the post-disaster operation and scheduling of the transportation system, and the repair decision constraints of the urban distribution network and the transportation system, as well as the repair decision-making constraints associated constraints, are combined to maximize the total power load recovery and road traffic flow as the objective function, and establish an optimization model for the coordinated repair scheduling of the urban distribution network and the transportation system after the disaster; Step S5: Using the data information obtained in step S1 as input parameters of the collaborative emergency repair scheduling optimization model, solving the collaborative emergency repair scheduling optimization model for the urban distribution network and transportation system after the disaster, and obtaining the emergency repair scheduling plan under the current step; Step S6: The urban power grid repair team and the transportation system repair team carry out collaborative repairs according to the repair scheduling plan. After completing the repairs in the current step, they return to step S1. If there are new faulty components or damaged roads, the fault data information is substituted into the collaborative repair scheduling optimization model established in step S4 and solved and executed until all faulty components and damaged roads resume normal operation.

2. The method for collaborative emergency repair scheduling of urban distribution network and transportation system after disaster according to claim 1, wherein, The network parameters in step S1 include the impedance of each power line in the distribution network, the maximum allowable active power carrying capacity, the maximum allowable traffic flow of each road in the traffic system, and the maximum allowable traffic flow of each parking place.

3. The method for post-disaster collaborative emergency repair scheduling of urban distribution network and transportation system according to claim 1 or 2, characterized in that, The balance constraint of active power and reactive power of the node is: Among them, s represents the current scheduling step number; is the active power of the generator connected to node i at the s-th step; represents a binary variable indicating whether node i is operating with power at the s-th step. If node i has power, it is 1; otherwise, it is 0; is the active load carried by node i; (i) represents the set of nodes connected to node i, is the active power flow on branch ij at the s-th step; is the reactive power of the generator connected to node i at the s-th step; is the reactive load carried by node i; is the reactive power flow on branch ij at the s-th step.

4. The method for collaborative emergency repair scheduling of urban distribution network and transportation system after disaster according to claim 3, wherein, The upper and lower operating limit constraints include: DistFlow linear power flow equation for the power distribution network power flow model: For the active power flow of branch ij and the reactive power flow They and the terminal voltages of the nodes at both ends of the branch and branch resistance r ij and branch reactance x ij satisfy the following relationship: In formula (3), M is a constant; It is a binary variable indicating whether the branch ij is energized and operating in the s-th step. If there is power flow on this branch, that is, it is energized, it is 1; otherwise, it is 0. The maximum allowable active power flow constraint for the branch is: Node voltage V i s The physical operating constraints are as follows: The active power constraint generated by the generator is as follows: The reactive power constraint generated by the generator is as follows: Among them, is the maximum active power that line ij can allow; V i max and V i min are respectively the upper and lower limits of the voltage allowed at node i; and are respectively the upper and lower limits of the active / reactive power output of the generator connected to node i.

5. The method for post-disaster collaborative repair and dispatch of urban distribution network and transportation system according to claim 4, characterized in that, The radial topology reconfiguration operation constraints of the distribution network include: 1) The urban distribution network is a radial network. During the post-disaster recovery process, the urban distribution network needs to maintain radial operation. Whether the downstream branch can be powered depends on the power supply status of the upstream branch to which it is connected. The following constraints are involved: Constraint that branch ij can be energized only when at least one of its upstream branches is powered: Radial network structure constraint derived based on graph theory: Among them, represents the energized state of an upstream branch hi of branch ij at the s-th step, where h is any node in the set of nodes connected to node i, being 1 if energized and running, otherwise 0; N line is the number of branches in the urban distribution network; N bus is the number of nodes, N source is the number of power sources in the distribution network; 2) During post-disaster restoration, constraints that once a failed node or branch is restored to power, it remains powered in subsequent scheduling steps include: Among them, is the charged state of node i at the (s + 1)-th step, is the energized operating state of branch ij at the (s + 1)-th step.

6. The method for post-disaster collaborative emergency repair scheduling of urban distribution network and transportation system according to claim 5, characterized in that, The constraint conditions for the post-disaster operation scheduling of the transportation system are: The upper and lower limits of the traffic flow that can be accommodated at the parking locations (i.e., nodes) and on the roads (i.e., branches) of the transportation system are 0, and the constraint is expressed as: Among them, is the maximum traffic flow that can be accommodated at traffic system node o; is a binary variable representing the traffic state of traffic system branch od at the s-th step. If it is 1, it means the road is unobstructed; otherwise, it is 0; represents the maximum traffic flow that can be accommodated on road od at the s-th step; Constraint conditions for the traffic flow turning in or out on the road: Constraint conditions for the traffic flow turning in or out on the road:

7. The method for post-disaster collaborative emergency repair scheduling of urban distribution network and transportation system according to claim 5 or 6, characterized in that The respective repair decision constraints of the urban distribution network and the transportation system include Constraint conditions for power lines without faults and traffic roads without damage: Among them, the binary emergency repair decision variable and When the emergency repair team c of the distribution network repairs the power branch ij at the s-th step, the emergency repair decision variable is 1, otherwise it is 0; when the emergency repair team z of the transportation system repairs the interrupted road od at the s-th step, the emergency repair decision variable is 1, otherwise it is 0; P normal represents the set of power lines without faults, and F normal represents the set of transportation roads that are not damaged and interrupted; Constraint conditions that whether a faulty line and a damaged road can return to normal operation depends on whether they are repaired: wherein, is a binary variable indicating whether the branch ij is energized and operating at the s-th step; is a binary variable for the traffic status of the branch od at the s-th step; Constraint condition that the number of lines and roads that the repair teams of the power system and the transportation system can repair within each scheduling interval is limited: Among them, N c is the number of lines that the emergency repair team of the power system can repair within a dispatching area, and P damage is the set of faulty lines in the power system; N z is the number of roads that the emergency repair team of the transportation system can repair within a dispatching area, and F damage is the set of interrupted roads in the transportation system; Constraint related to the repair decision of the power system repair team restricted by the road operation state: Among them, is a binary variable, which is 1 when there is a passable path f to ensure that the power system emergency repair team c departs from the station to the faulty line ij at the s-th step, and 0 otherwise; When all the roads included in any path f are in a passable state, the distribution network repair team implements the repair behavior. This dependency relationship is expressed based on an improved Boolean logic expression. Suppose there are two paths f1 and f2, and the implementation of the repair behavior can be ensured as long as any one of them is passable. The roads included in path f1 are ox, dy, dz, and the roads included in path f2 are on, dm. At this time, equation (31) is further expressed in detail: Assume that roads ox, dy, and on are all normally passable roads, and their state variables take the value of 1. Therefore, Equation (32) is further simplified in practice to It is used to indicate that whether the emergency repair decision can be executed only depends on whether the passing path contains a faulty road.

8. The method for collaborative emergency repair scheduling of urban distribution network and transportation system after disaster according to claim 7, characterized in that, The objective function is: where w is the weight coefficient of the power system, N bus is the number of nodes in the power system, N road is the number of roads in the transportation system; The collaborative repair scheduling optimization model for the urban distribution network and the transportation system after the disaster is: Objective function: Equation (33); Constraint conditions: Equations (1)-(31).

9. The method for post-disaster collaborative emergency repair scheduling of urban distribution network and transportation system according to claim 8, characterized in that, The collaborative repair scheduling optimization model is a mixed-integer linear programming model, and is solved using commercial optimization solvers such as Gurobi or Mosek.

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