Global optimization allocation method and system for mobile emergency resources and medium

Abstract

The invention relates to a mobile emergency resource global optimization allocation method and system and a medium, and the method comprises the steps: obtaining structure parameters of a post-disaster traffic system, carrying out the modeling of a traffic network, and dividing roads in the traffic network into a plurality of uniform cells based on a cell transmission model; in a traffic network, minimum vehicle weighted driving time is taken as an objective function, traffic flow conservation constraint is taken as a constraint condition, different weights are given according to importance degrees of different types of mobile emergency resources, and an emergency resource scheduling model based on dynamic traffic flow is constructed. The driving paths and the driving time of all the mobile emergency resources are obtained after solving; and constructing a power distribution network recovery model by taking the maximum load supply quantity as a target function, and substituting the driving paths and the driving time of all the mobile emergency resources for solving to obtain a power distribution network recovery scheme. Compared with the prior art, power generation resources can be fully utilized, so that emergency power supply service is provided for more loads.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a method, system and medium for global optimization allocation of mobile emergency resources. Background Technology

[0002] When using mobile resources for fault recovery after extreme events, it is necessary to consider the real-time operating status of the transportation system to formulate a mobile emergency resource allocation plan in the transportation network. There are two challenges in formulating a power distribution network recovery plan that takes into account the scheduling of mobile emergency resources in the transportation network: (1) how to simulate the impact of real-time traffic conditions on the scheduling of mobile emergency resources; (2) how to quickly formulate recovery decisions to achieve online real-time emergency response.

[0003] For example, the invention disclosed in CN113346488A discloses a method for restoring urban distribution networks that considers mobile emergency resource dispatching, including: establishing a mobile power generation resource constraint model considering the constraint of the time for mobile power generation resources to reach the fault location; establishing a topology constraint model considering the constraint of maintenance personnel repairing the lines; establishing a multi-period urban distribution network fault recovery model that considers mobile emergency resource dispatching with the objective of maximizing the weighted power supply time of the load, and with operational constraints, the mobile power generation resource constraint model, the topology constraint model, and load state constraints as constraints; solving the multi-period urban distribution network fault recovery model that considers mobile emergency resource dispatching, and performing mobile emergency resource dispatching on the urban distribution network based on the solution results.

[0004] In this scheme, the mobile power generation resource constraint model is established directly based on the constraint of the time when the mobile power generation resources arrive at the fault location, without considering the spatiotemporal distribution of traffic flow; and the multi-period urban distribution network fault recovery model is established with the goal of maximizing the weighted power supply time of the load, without considering the total load recovery, and thus failing to make full use of power generation resources. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art, which does not consider the spatiotemporal distribution of traffic flow and cannot fully utilize power generation resources, and to provide a method, system and medium for global optimization and allocation of mobile emergency resources.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for global optimization and allocation of mobile emergency resources includes the following steps: The structural parameters of the post-disaster transportation system are obtained, the transportation network is modeled, and based on the cellular transport model, the roads in the transportation network are divided into multiple uniform cells to describe the dynamic changes of traffic flow. In the aforementioned transportation network, the objective function is to minimize the weighted travel time of vehicles, the constraint is to conserve traffic flow, and different weights are assigned according to the importance of different types of mobile emergency resources. An emergency resource scheduling model based on dynamic traffic flow is constructed, and the travel paths and travel times of all mobile emergency resources are obtained after solving the model. A power distribution network restoration model is constructed with the objective function of maximizing load supply. The model is then solved by substituting the travel paths and travel times of all mobile emergency resources to obtain the power distribution network restoration scheme.

[0007] Furthermore, the length of the cell is the distance traveled by the vehicle per unit time period at free speed.

[0008] Furthermore, the expression describing the dynamic changes of traffic flow using the cellular transport model is as follows: In the formula, Represents cell exist Traffic flow at any given time Represents a cell exist Traffic flow at any given time Indicates in Time by cell Flow to cells Traffic flow Indicates in Time by cell Flow to cells Traffic flow Represents all cells A collection of connected upstream cells. Represents all cells A collection of connected downstream cells. The set consisting of all cells. It is the set of all time periods within a time window; Cell exist The maximum flow rate that can flow in or out at any given time. Represents cell exist The state of constant congestion during transmission. , For cells exist The free-moving speed of the vehicle at any given time. For cells exist The speed of vehicles under the influence of traffic congestion at any given time. for Time can be stored in a cell The maximum traffic flow.

[0009] Furthermore, the cellular transport model is applied to the flow of traffic in post-disaster transportation systems. and density It is constructed when the cell transport constraint is satisfied, and the expression of the cell transport constraint is: In the formula, This refers to the free-moving speed of vehicles on this section of road. This represents the maximum traffic volume on this road section. The speed at which vehicles travel under the influence of traffic congestion. This represents the maximum vehicle density, also known as congestion density.

[0010] Furthermore, the expression for the emergency resource scheduling model based on dynamic traffic flow is: In the formula, The objective function value, , These are sets of different types of vehicles and the connections between cells, respectively. A set consisting of terminating cells; Indicates the first Priority weights for vehicle classes; Represents cell exist The first moment Traffic flow of this type of vehicle; Indicates in Time by cell Flow to cells The Traffic flow of this type of vehicle; Represents the initial time cell The Traffic flow of this type of vehicle Connecting cells at the initial time step Interval Traffic flow of this type of vehicle.

[0011] Furthermore, the different types of vehicles include mobile emergency power supplies, maintenance personnel, dispatchable public buses, and regular vehicles.

[0012] Furthermore, the objective function of the power distribution network restoration model is expressed as follows: In the formula, To optimize the number of problem time periods, The number of system nodes. For nodes Unit load value For nodes At the moment The active load supply of all mobile emergency resources.

[0013] Furthermore, the constraints of the distribution network recovery model include fixed generation resource output constraints, mobile generation resource output constraints, power flow constraints, and distribution network radial constraints.

[0014] The present invention also provides a mobile emergency resource global optimization and allocation system, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor using the method described above.

[0016] Compared with the prior art, the present invention has the following advantages: (1) This invention proposes to divide the roads in the traffic network into several uniform cells by using a cell transmission model. It assumes that there is a linear relationship between the flow rate and density of the traffic flow in the cell, thereby describing the dynamic changes of the traffic flow. Thus, an emergency resource scheduling model based on dynamic traffic flow is constructed. The traffic flow of each cell is weighted, and different weights can be assigned to different types of vehicles according to their importance to the emergency response. In addition, traffic flow conservation constraints, cell movement flow constraints, and initial state constraints of traffic flow in cells and connecting lines are added to the emergency resource scheduling model, thereby solving for the travel paths and travel times of all mobile emergency resources. Furthermore, by using a distribution network restoration model to maximize load supply, various resources are regulated to obtain the final distribution network restoration plan. This method comprehensively considers traffic conditions and aims to maximize load supply, which is conducive to making full use of power generation resources and thus providing emergency power supply services for more loads.

[0017] (2) The constraints of the emergency resource scheduling model and the power distribution network recovery model based on dynamic traffic flow in this invention fully consider the traffic flow status. Compared with the shortest path scheduling method, which only considers the distance between different nodes and does not consider traffic congestion, it can better schedule mobile emergency resources. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for global optimization and allocation of mobile emergency resources provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a traffic-power distribution coupling system after an extreme event occurs, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a key road in a transportation network provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a transportation network cell partitioning result provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the load recovery result obtained by the proposed method in an embodiment of the present invention; Figure 6 This is a schematic diagram of the load recovery result obtained by shortest path scheduling in an embodiment of the present invention; Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] Example 1 like Figure 1As shown, this embodiment provides a method for global optimization and allocation of mobile emergency resources, including the following steps: S1: Obtain the structural parameters of the post-disaster transportation system, model the transportation network, and based on the cell transmission model, divide the roads in the transportation network into multiple uniform cells to describe the dynamic changes of traffic flow. S2: In the transportation network, with the objective function of minimizing the weighted travel time of vehicles and the constraint of traffic flow conservation, and different weights assigned according to the importance of different types of mobile emergency resources, an emergency resource scheduling model based on dynamic traffic flow is constructed. After solving the model, the travel paths and travel times of all mobile emergency resources are obtained. S3: Using the maximization of load supply as the objective function, construct a power distribution network restoration model, and substitute the travel paths and travel times of all mobile emergency resources to solve the problem and obtain the power distribution network restoration scheme.

[0023] Specifically, the process of constructing the cell transport model in step S1 is as follows: As a fluid, traffic flow can be described by the differential Lighthill-Whitham-Richards equations for its density, velocity, and flow rate. The cellular transport model is a discrete-time approximation of these equations. It describes the dynamic changes of traffic flow by dividing the roads in the traffic network into several uniform cells and assuming a linear relationship between the flow rate and density of traffic in the cells.

[0024] Cellular models show that if the flow and density If equation (1) is satisfied, then time can be divided into several segments, and all edges in the transportation network can be divided into several cells. Within a specific time window, the length of the cell is the distance traveled by the vehicle at free speed per unit time period.

[0025] (1) in, This refers to the free-moving speed of vehicles on this section of road. This represents the maximum traffic volume on this road section. The speed at which vehicles travel under the influence of traffic congestion. This represents the maximum vehicle density, also known as congestion density.

[0026] Based on the cellular transport model, the dynamic changes in traffic flow can be described by the following expression: (2) (3) (4) in, Represents cell exist Traffic flow at any given time Represents cell exist Traffic flow at any given time Indicates in Time by cell Flow to cells Traffic flow Indicates in Time by cell Flow to cells Traffic flow Represents all cells The set of connected upstream cells (for the source cell, ), Represents all cells The set of connected downstream cells (for terminating cells, ), The set consisting of all cells. It is the set of all time periods within a time window; Cell exist The maximum flow rate that can flow in or out at any given time (this value can be set to infinity for source and terminal cells). Represents cell exist The state of constant congestion during transmission. Formula (2) is the traffic flow balance equation within a cell, and (3) and (4) are variations of formula (2).

[0027] In step S2, the process of constructing the emergency resource scheduling model based on dynamic traffic flow is as follows: In the transportation network, there are four types of vehicles after extreme events: mobile emergency power supplies, maintenance personnel, dispatchable public buses, and ordinary vehicles. Different weights can be assigned to different types of vehicles based on their importance to the emergency response. The mobile emergency resource optimization scheduling model based on improved dynamic traffic allocation takes minimizing vehicle weighted travel time as its objective function, with constraints including traffic flow conservation constraints and constraints related to equation (1). The specific model is as follows: (5) (6) (7) (8) (9) (10) (11) in, , These are sets of different types of vehicles and the connections between cells, respectively. A set consisting of terminating cells; Indicates the first Priority weights for vehicle classes; Represents cell exist The first moment Traffic flow of this type of vehicle; Indicates in Time by cell Flow to cells The Traffic flow of this type of vehicle; Represents the initial time cell The Traffic flow of this type of vehicle Connecting cells at the initial time step Interval Traffic flow for all vehicle types. Equation (5) represents minimizing the total weighted travel time for all vehicle types. Within each cell, the travel time of a vehicle is non-negatively correlated with the vehicle density within the cell; therefore, the objective function represents minimizing the travel time. Constraint (6) represents the traffic flow conservation constraint. Equation (7) represents the flow from the cell... Types that move to other cells The total traffic flow is no greater than a cell Types in The total traffic flow. Equation (8) represents the total traffic flow from the cell. The total flow of moving to other cells is no greater than that of the cell. The maximum capacity. Equation (9) shows the movement from other cells to a cell. The total flow is no greater than the cell The maximum capacity. Equation (10) indicates that the cell The total inflow does not exceed the available remaining occupancy rate of the cell. This constraint is used to handle situations when traffic is congested. Equation (11) represents the initial state of traffic flow in the cell and the connecting line. In the initial state, ordinary vehicles are not in the source cell and the terminal cell, indicating that they are on the road when extreme events occur, while other emergency vehicles are in the source cell.

[0028] In summary, an emergency resource optimization scheduling model based on improved dynamic traffic allocation was established. This model is linear and can be solved quickly using a mature commercial optimization solver. After solving, the travel paths and travel times of all mobile emergency resources can be obtained.

[0029] In step S3, the specific process of solving the distribution network restoration scheme is as follows: Solving optimization problems (5) to (11) yields the arrival times of each mobile emergency resource at its destination. These times are then incorporated into the power distribution network restoration model in step S2 to obtain the power distribution network restoration strategy. The power distribution network restoration model in step S2 aims to maximize load supply and regulates various resources as follows: Fixed power generation resource output constraints Mobile resource scheduling constraints Mobile power generation resource output constraints Current constraints Radial constraints of distribution network This mixed-integer problem can be solved using commercial solvers such as GUROBI to obtain power grid restoration solutions. It fully considers the impact of dynamic traffic flow on power grid restoration and has practical application value.

[0030] The specific constraints on stationary power generation resources include: Conventional power output constraints: Conventional power output mainly considers factors such as power limitations and reserve capacity, and the specific constraints are as follows: In the formula: For nodes Conventional power supply at all times contribution; For nodes Conventional power supply at any time Rotate upwards / downwards to reserve quantity; For nodes Upper / lower output limits of conventional power supplies; For nodes The first formula represents the maximum ramping capacity of a conventional power source per unit time period; the second formula represents the power output constraint considering spinning reserve. The third formula represents the power source ramping capacity constraint.

[0031] New energy power output constraints: The power output of new energy sources needs to take into account their maximum power output curve, therefore, their power output constraints are as follows: In the formula: For nodes Wind power / solar power at time Actual output; For nodes Wind power / solar power at time The maximum output. The above two constraints are the output constraints for wind power and photovoltaic power, respectively.

[0032] Energy storage operation constraints: The operation of energy storage needs to consider charge and discharge constraints as well as power constraints, therefore the constraints are as follows: In the formula: Representing nodes respectively Energy storage at all times The charging / discharging power; For nodes Maximum charge / discharge power of energy storage; Represents a node Energy storage at all times The amount of electricity; For nodes The maximum capacity of energy storage; The above three formulas represent the charging and discharging efficiency of energy storage, respectively, the charging and discharging power constraints, the energy constraints, and the energy equation.

[0033] The aforementioned mobile resource scheduling constraints specifically include mobile power supply scheduling constraints and emergency repair team scheduling constraints. Mobile power supply scheduling constraints include: In the formula: A set of mobile power bank access points; For the scene Downstream power bank During the period From the pre-disaster deployment point Scheduled to node state, =1 indicates that the node will be scheduled to this node. =0, then the opposite is true; These are known quantities in each scenario, obtained during the scenario generation phase, representing quantities from the pre-disaster deployment point. To the node The minimum required travel time. The first formula above indicates that any mobile power bank can only be dispatched to at most one access point; the second formula indicates that only one mobile power bank is allowed to connect to one access point; the third formula indicates that once a mobile power bank is dispatched to any access point, it will not move further; the fourth formula indicates the scenario. Downstream power bank During the period Access point not reached At that time, this access point does not have the ability to restore load.

[0034] The constraints on dispatching emergency repair teams include: In the formula: For the scene The following is a collection of damaged lines; For 0-1 variables, =1 indicates a scenario From the deployment point The emergency repair team set off During the period Dispatch to the damaged location. =0, then the opposite is true; , For any damaged line; For the repair team from the line arrive The travel time; The number represents the time period. The first formula above indicates that any repair team can only be dispatched to a maximum of one damaged line; the second formula indicates that only one repair team needs to be dispatched to repair a damaged line; the third formula indicates that when repair teams are dispatched sequentially, a certain amount of travel time is required before they can move to another line.

[0035] The aforementioned power flow constraints include line state constraints, node power balance constraints, voltage equations, and line flow constraints, as detailed below: In the formula: For the line At the moment The running status, Indicates the line At the moment closure, Indicates the line At the moment Break; For the set of faulty lines, A set of tie lines that can be controlled to open and close; For the line At the moment Active / reactive flow; For nodes At the moment The active / reactive load supply; These represent the beginning and end nodes respectively. A collection of routes; For nodes At the moment Load demand; For nodes At the moment The voltage amplitude; For the line Resistance / Reactance; The reference voltage; It is a sufficiently large positive number; For nodes Upper / lower voltage limits; The lines are respectively The active / reactive flow limits are defined by the following formulas: The first formula represents the line state constraint, where a faulty line is open, while lines without switches in operation remain closed; the second and third formulas represent node power balance constraints; the fourth formula indicates that the load supply does not exceed the load demand; the fifth formula is the linearized voltage equation; the sixth formula represents the voltage amplitude constraint; and the seventh formula represents the line flow constraint.

[0036] The aforementioned radial constraint on the distribution network pertains to the load recovery phase following extreme events. The distribution network can improve resource utilization and load supply rate through topology reconfiguration and other methods. The adjusted topology still needs to maintain a radial shape. The specific constraints are as follows: In the formula: Indicates that by node To the node Virtual power flow; For the set of lines in the system, A set of nodes; The set of root nodes; A sufficiently large positive number can be taken as the number of system nodes. ; Let represent the number of root nodes. The first formula above indicates that the virtual power supply of non-root nodes is 1; the second formula models the virtual power flow based on the line operating status; the third formula indicates that the number of operating lines equals the number of non-root nodes. These constraints ensure the connectivity of the power grid and allow it to operate in a radial topology.

[0037] Case Analysis In such Figure 2 The proposed method is validated in the power distribution-traffic coupling system shown. The traffic system has 12 nodes and 20 lines. Node 1 represents the material center, the starting point for mobile emergency generators, mobile energy storage systems, and maintenance personnel. The three rectangles are electric bus stops, i.e., the starting points for electric buses. Nodes 6 and 7 are the destinations for mobile emergency generators, node 9 is the destination for mobile energy storage devices, and node 10 is the destination for maintenance personnel. Table 1 provides the basic parameters of the traffic system. "Maximum flow" represents the maximum traffic volume, measured in vehicles per hour (vph). The table shows that the outer ring expressway can handle a larger traffic volume.

[0038] Table 1 Basic parameters of the transportation system In the distribution network system, the maximum output of distributed power sources at nodes 1, 4, 12, and 23 are 600, 100, 150, and 100 kW, respectively. The power load is divided into three levels: the loads at nodes 3, 12, and 33 are level 1 loads with a weighting factor of 100; the loads at nodes 15, 17, 24, and 30 are level 2 loads with a weighting factor of 10; and the others are ordinary loads with a weighting factor of 0.2.

[0039] Assume that after a fault, two highways between nodes 6 and 10 in the transportation system are damaged, the power distribution system loses power and is disconnected from the substation. Two faults occur at nodes 32 and 29, forming a fault zone consisting of nodes 29, 31, and 30 and the lines between them. Furthermore, assume the estimated restoration time of the main power grid is 4 hours. Based on the above example parameters, the method proposed in this section is validated.

[0040] In the process of mobile emergency resource dispatching strategy processing, the transportation network is modeled according to the travel direction of the mobile resources, as follows: Figure 3 As shown, the system is divided into cells to determine the dispatching scheme for mobile emergency resources. The bolded lines are critical roads affecting the dispatching of mobile emergency resources within the transportation network. A time interval of 5 minutes, a cell length of 6 kilometers, and a time window of 1 hour are used. The transportation system is divided into 41 cells, as shown below. Figure 4 As shown.

[0041] exist Figure 4In the table, the starting point and destination of emergency resources are marked with different colors, corresponding to the source cell and the termination cell, respectively. Based on the information in Table 1, the relevant parameters of the cells can be obtained, including the maximum traffic flow that can be stored in the cell. and the maximum traffic flow that can flow from the cell inflow and outflow .

[0042] Assuming an extreme event occurs, causing traffic congestion on Highways 1 and 2 due to an accident, the proposed improved dynamic traffic assignment model is used to obtain the optimal emergency resource scheduling strategy. The results are shown in Table 2. It can be seen that all vehicles depart 5 minutes later, due to the time spent on decision-making, issuing commands, and preparing for departure. This demonstrates that the proposed scheduling method can consider the real-time traffic flow status of the transportation network, avoid congested routes, and formulate reasonable scheduling paths for mobile emergency resources.

[0043] Table 2. Routes and Arrival Times of Various Mobile Emergency Resources The processing steps of the dynamic recovery strategy for the distribution network include: Based on the calculation results of the mobile emergency resource dispatch strategy, a multi-period fault recovery strategy for the power distribution system was obtained. The entire power outage time was divided into 48 periods. Figure 5 and Figure 6 Load recovery curves obtained by the proposed mobile resource scheduling method and the shortest path scheduling method were compared. The shortest path scheduling method only considers the distance between different nodes and does not account for traffic congestion. In the shortest path-based scheduling scheme, the travel path to the mobile generator at destination 6 becomes 1-2-6. The results show that the total load recovery corresponding to the proposed method is 6244 kWh, while the total load recovery based on shortest path scheduling is 6076.42 kWh. Therefore, the emergency resource scheduling method that considers traffic conditions proposed in this scheme is beneficial for fully utilizing power generation resources, thereby providing emergency power supply services to more loads.

[0044] Example 2 This embodiment provides a mobile emergency resource global optimization and allocation system, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method in Embodiment 1.

[0045] Example 3 This embodiment provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor as described in Embodiment 1.

[0046] The computer program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This computer program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the computer program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0047] Figure 7 A schematic block diagram of an electronic device that can be used to implement embodiments of the present disclosure is shown. Figure 3 As shown, the electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0048] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0049] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0050] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for global optimization and allocation of mobile emergency resources, characterized in that, Includes the following steps: The structural parameters of the post-disaster transportation system are obtained, the transportation network is modeled, and based on the cellular transport model, the roads in the transportation network are divided into multiple uniform cells to describe the dynamic changes of traffic flow. In the aforementioned transportation network, the objective function is to minimize the weighted travel time of vehicles, the constraint is to conserve traffic flow, and different weights are assigned according to the importance of different types of mobile emergency resources. An emergency resource scheduling model based on dynamic traffic flow is constructed, and the travel paths and travel times of all mobile emergency resources are obtained after solving the model. A power distribution network restoration model is constructed with the objective function of maximizing load supply. The model is then solved by substituting the travel paths and travel times of all mobile emergency resources to obtain the power distribution network restoration scheme.

2. The method for global optimization and allocation of mobile emergency resources according to claim 1, characterized in that, The length of the cell is the distance traveled by the vehicle per unit time period when it travels at free speed.

3. The method for global optimization and allocation of mobile emergency resources according to claim 1, characterized in that, The expression describing the dynamic changes in traffic flow using the cellular transport model is as follows: In the formula, Represents cell exist Traffic flow at any given time Represents cell exist Traffic flow at any given time Indicates in Time by cell Flow to cells Traffic flow Indicates in Time by cell Flow to cells Traffic flow Represents all cells A collection of connected upstream cells. Represents all cells A collection of connected downstream cells. The set consisting of all cells. It is the set of all time periods within a time window; Cell exist The maximum flow rate that can flow in or out at any given time. Represents cell exist The state of constant congestion during transmission. , For cells exist The free-moving speed of the vehicle at any given time. For cells exist The speed of vehicles under the influence of traffic congestion at any given time. for Time can be stored in a cell The maximum traffic flow.

4. The method for global optimization and allocation of mobile emergency resources according to claim 3, characterized in that, The cell transport model in the post-disaster transportation system flow and density It is constructed when the cell transport constraint is satisfied, and the expression of the cell transport constraint is: In the formula, This refers to the free-moving speed of vehicles on this section of road. This represents the maximum traffic volume on this road section. The speed at which vehicles travel under the influence of traffic congestion. This represents the maximum vehicle density, also known as congestion density.

5. The method for global optimization and allocation of mobile emergency resources according to claim 3, characterized in that, The expression for the emergency resource scheduling model based on dynamic traffic flow is: In the formula, The objective function value, , These are sets of different types of vehicles and the connections between cells, respectively. A set consisting of terminating cells; Indicates the first Priority weights for vehicle classes; Represents cell exist The first moment Traffic flow of this type of vehicle; Indicates in Time by cell Flow to cells The Traffic flow of this type of vehicle; Represents the initial time cell The Traffic flow of this type of vehicle Connecting cells at the initial time step Interval Traffic flow of this type of vehicle.

6. The method for global optimization and allocation of mobile emergency resources according to claim 5, characterized in that, The different types of vehicles include mobile emergency power supplies, maintenance personnel, dispatchable public buses, and regular vehicles.

7. The method for global optimization and allocation of mobile emergency resources according to claim 1, characterized in that, The objective function of the power distribution network restoration model is expressed as follows: In the formula, To optimize the number of problem time periods, The number of system nodes. For nodes Unit load value For nodes At the moment The active load supply of all mobile emergency resources.

8. The method for global optimization and allocation of mobile emergency resources according to claim 1, characterized in that, The constraints of the distribution network recovery model include fixed generation resource output constraints, mobile generation resource output constraints, power flow constraints, and distribution network radial constraints.

9. A mobile emergency resource global optimization and allocation system, characterized in that, It includes a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor according to any one of claims 1 to 8.

Images