CN117294577A

CN117294577A - Method and system for quickly recovering information physical collaboration of elastic power distribution network

Info

Publication number: CN117294577A
Application number: CN202311237932.3A
Authority: CN
Inventors: 杨祺铭; 陈晨; 邹文秋; 刘达夫; 钟剑; 李明昊; 李更丰; 别朝红; 秦玉文; 胡骞文; 孙少华; 辛正堃; 刘超; 徐铭乾; 王毅钊; 刘浩; 邵美阳; 王露缙
Original assignee: Xian Jiaotong University; Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
Current assignee: Xian Jiaotong University; Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
Priority date: 2023-09-22
Filing date: 2023-09-22
Publication date: 2023-12-26

Abstract

The invention discloses a method and a system for information physical collaboration rapid recovery of an elastic power distribution network, wherein an EV pre-disaster scheduling model is established; establishing a pre-disaster stage preparation model based on the EV pre-disaster scheduling model to realize pre-disaster preparation; determining the communication coverage of the unmanned aerial vehicle based on the suspension position of the unmanned aerial vehicle and environmental factors, and simultaneously considering that a telemechanical switch and a V2G site in a recovery area are covered by communication to establish a regional unmanned aerial vehicle communication demand model; determining EV refuge results based on the regional unmanned aerial vehicle communication demand model and the pre-disaster stage preparation model, and establishing a post-disaster stage dynamic recovery model with the minimum of the system weighted load loss of the whole recovery period as a target; and acquiring the fault scene information of the power distribution network based on the post-disaster stage dynamic recovery model, and realizing post-disaster recovery. The invention fully researches the flexible resource association relation between the information layer and the physical layer under the information physical coupling characteristic, quickly and efficiently recovers the information physical system of the power distribution network, and reduces the power failure loss to the maximum extent.

Description

Method and system for quickly recovering information physical collaboration of elastic power distribution network

Technical Field

The invention belongs to the technical field of intelligent power distribution networks, and particularly relates to an elastic power distribution network information physical collaboration quick recovery method and system.

Background

The intelligent power distribution network is a new mode of power distribution network development, and the power distribution automation function is realized through the deep fusion of a power network and a communication network, so that the terminal power supply is stable and efficient, and the intelligent power distribution network is a typical information-physical fusion system (CPS). In recent years, the intensity and frequency of natural disasters occurring worldwide have increased significantly, and power distribution networks exhibit characteristics of inadequate preparation and extreme vulnerability under the impact of small probability-high loss extreme disasters which are difficult to predict. Due to the spatial correlation, the faults of the information layer and the physical layer equipment of the power distribution network are often caused at the same time when disasters occur, the information-physical coupling and interaction characteristics can cause the propagation and evolution of the faults, the power failure range is enlarged, and the power recovery time is delayed. Therefore, the coupling characteristic and evolution mechanism between the information layer and the physical layer recovery are necessary to be considered, the adjustment capability of flexible resources of the information layer and the physical layer is deeply excavated, the elastic improvement method of the power distribution system is researched, and the defect that the existing power supply recovery and emergency measures cope with extreme natural disasters is overcome.

With the recent popularization of Electric Vehicles (EVs), it is possible for the EVs to participate in power recovery of a distribution network in a vehicle-to-grid (V2G) manner after a disaster, and researches prove that compared with other mobile power generation resources, the EVs participate in power recovery, so that the economic cost of the system can be reduced. The power supply recovery efficiency of the distribution network depends on the cooperative cooperation of the control topology reconstruction of a feeder automation terminal unit (feeder terminal unit, FTU) and the source, load and storage of distribution automation to a great extent, the distribution automation function is normally based on the premise that a system provided by an information layer is considerable and controllable, but the prior research usually neglects the damage condition of a communication network, and considers that the control and the scheduling of flexible resources of a physical layer are not affected, which is not in accordance with the actual condition after disaster.

After the communication system fails, the temporary deployment of the wireless network is an effective measure, and necessary communication support can be provided for post-disaster rush repair and rescue work and system state information acquisition so as to accelerate the recovery of the power grid. Unmanned aerial vehicles (unmanned aerial vehicle, UAVs) can be rapidly deployed and construct temporary communication networks as flexible resources of an information layer, so that partial recovery of system observability and controllability is realized, potential of the unmanned aerial vehicles in power distribution communication system recovery after extreme disasters is explored by the existing team, however, coordination and cooperation of flexible resources of a physical layer are not considered in the researches, and beneficial effects of information physical cooperation on power distribution network elasticity cannot be fully reflected.

Disclosure of Invention

The technical problem to be solved by the invention is to provide a method and a system for recovering information physical collaboration of an elastic power distribution network rapidly aiming at the defects in the prior art, which are used for solving the technical problems of allocating communication unmanned aerial vehicle and EV to recover the information physical system of the power distribution network so as to cope with the challenges of partial line damage, insufficient power supply and the like of the power distribution network after disaster and improve the recovery efficiency of the power distribution network after disaster.

The invention adopts the following technical scheme:

a physical collaboration quick recovery method for information of an elastic power distribution network comprises the following steps:

S1, establishing an EV pre-disaster scheduling model;

s2, establishing a pre-disaster stage preparation model based on the EV pre-disaster scheduling model obtained in the step S1, so as to realize pre-disaster preparation;

s3, determining the communication coverage of the unmanned aerial vehicle based on the suspension position of the unmanned aerial vehicle and environmental factors, and simultaneously considering that a telemechanical switch and a V2G site in a recovery area are covered by communication to establish a regional unmanned aerial vehicle communication demand model;

s4, determining EV refuge results based on the regional unmanned aerial vehicle communication demand model obtained in the step S2 and the pre-disaster stage preparation model obtained in the step S3, and establishing a post-disaster stage dynamic recovery model with the minimum system weight loss load of the whole recovery period as a target;

s5, acquiring the fault scene information of the power distribution network based on the post-disaster stage dynamic recovery model obtained in the step S4, and realizing post-disaster recovery.

Specifically, the pre-EV disaster scheduling model is specifically:

road network model:

G＝(V,E)

v is a node set in the road network; e is a set of connection relations of all nodes in the road network;

EV pass chain model:

Chain＝{B ₀ ,B _f ,W _0f ,L _0f ,T ₀ ,T _f ,T _0f ,T _p }

wherein, chain is travel Chain set; b (B) ₀ Is the starting point of the travel chain, B _f For the end point, W of the travel chain _0f For the travel chain line L _0f Is the length of the line, T ₀ For the departure time, T _f For the arrival time, T _0f For driving time, T _p For the residence time;

T _f,n ＝T _0,n +T _0f,n

T _0,n+1 ＝T _f,n +T _p,n

wherein T is _0f,n T for driving EV on the nth stage route _f,n For EV reaching the end time of the nth section line, T _0,n+1 D is the departure time of the EV n+1th section line _n Is the length of the nth segment of line; v' is EV average driving speed;

EV state model:

EVstatus＝{Cap _rem,t ,w _t ,P _t }

wherein EVstatus is a set of EV real-time status information, cap _rem,t For each EV remaining power, w _t For each EV driving state, P _t Numbering each EV distance from the nearest refuge station;

EV response ratio model:

N _res ＝ρN _total

wherein N is _res EV number for participation in refuge response; n (N) _total ρ is the evacuation response coefficient for the total EV number in the system.

Further, constraint conditions of the pre-EV disaster scheduling model comprise traffic network constraint, EV travel chain constraint, EV state constraint and EV response willingness constraint.

Specifically, the pre-disaster preparation is specifically as follows:

dividing a power distribution network into a plurality of areas according to the communication coverage range of a ground base station, wherein all telemechanical switches in each area are provided with communication support by the same base station;

modeling the emergency communication requirement of the unmanned aerial vehicle after each regional disaster, and realizing the full coverage of the regional communication network with the minimum number of unmanned aerial vehicles;

the ITS broadcasts disaster information in advance, the EV running in the urban road stops free dispatching and goes to the nearest refuge site for refuge, and unmanned aerial vehicle equipment managed by the emergency command center enters an emergency standby state.

Specifically, the regional unmanned aerial vehicle communication demand model is as follows:

s.t.

wherein p is _m Is a 0-1 variable; h is a _i,m A 0-1 variable, representing whether the communication node i is covered by the unmanned aerial vehicle at point m; d, d _i,m Is the straight line distance at communication node i and point m; x and y respectively represent the abscissa and the ordinate of the node; c is a set of communication nodes; m is the set of unmanned operating points.

Specifically, the post-disaster stage dynamic recovery model is specifically:

objective function

Where m is the current period; t is the total number of time periods involved in recovering the full cycle; b is a distribution network node set; omega _i Is the importance of the load connected with the ith node;a 0-1 variable representing whether the load of the ith node is accessed in the t-th period;is the load amount of the ith node in the t-th period;

unmanned aerial vehicle dispatch model

Spatial constraint:

wherein x is _i,j,c 0-1 variable representing the c-th unmanned plane from the working point i to j; u is a set of all unmanned aerial vehicles; m is a set of working points; s and R respectively represent a starting point set and a final point set of the unmanned aerial vehicle; y is _i,c Is a variable from 0 to 1; n is a set of powered down communication base stations; mat (Mat) _i Is the capacity required by the ith EV transmission data;is the communication capacity of the drone c;

time constraint:

wherein,the moment when the unmanned plane c reaches the working point i; / >Is the time that the unmanned aerial vehicle c stays at the working point i;the transit time of the unmanned aerial vehicle c from the working point i to the working point j;

EV scheduling model

Space-time constraint:

wherein,is a variable from 0 to 1; />Rescheduling time consuming in the road traffic network for the ith EV; n (N) _V2G Representing the number of V2G sites; e is an electric automobile set; v is a V2G site set; t is a recovery full period set;

number constraint:

wherein N is _EV Indicating the number of EVs involved in rescheduling; n (N) _i,VE Is the number of EVs that the ith V2G site can accommodate the maximum;

dynamic recovery model of power distribution network

Topology constraints:

-M·d _k,t ≤f _k,t ≤M·d _k,t ,k∈L,t∈T

-M·(1-d _k,t )+f _j,t ≤f _i,t ≤M·(1-d _k,t )+f _j,t

wherein d _k,t Is a 0-1 variable, and is the switching decision of the master station on the kth line; f (f) _k,t A commodity stream representing a kth line; f (f) _i,t Commodity flow requirements representing an ith node;commodity flow injection quantity representing power supply nodes; d (D) _k,i Is a 0-1 variable representing whether the kth line is connected to the ith node; l is a line set; g is a set of power supply nodes; f (F) _i Is a collection of lines connected to the ith node;

operation constraint:

p _k,t ≤p _k,max

q _k,t ≤q _k,max

-M·d _k,t ≤p _k,t ≤M·d _k,t ,k∈L,t∈T

-M·d _k,t ≤q _k,t ≤M·d _k,t ,k∈L,t∈T

0.9V _ref ≤V _i,t ≤1.1V _ref s

wherein p is _k,t And q _k,t Respectively representing the active power and the reactive power of the kth line;and->Representing the active and reactive load demands of the ith node, respectively; />And->Active and reactive injection representing the ith power supply node, respectively; / >A 0-1 variable for indicating whether the load of the ith node of the table is accessed; />And->Representing the maximum and minimum power of the V2G site to the grid.

Further, when an originally closed circuit is disconnected, at least one of the communication nodes at the two ends of the originally closed circuit needs to be controlled by the master station; when an originally opened line is closed, communication nodes at two ends of the originally opened line are communicated with a master station normally; the information physical coupling constraint is specifically as follows:

wherein,0-1 variable, h, being the initial switching state of the kth line _i,m,t For the controlled variable of the line i end, h _j,m,t D is the controlled variable at the j end of the line _k,t 0-1 variable for the decision on the kth line switch,/for the switch>Is a 0-1 variable of the initial switch state of the kth line.

Further, constraint conditions of the post-disaster stage dynamic recovery model comprise unmanned plane scheduling constraint, EV scheduling constraint, radial topology constraint of network reconstruction, power distribution network operation constraint and information physical coupling constraint.

Specifically, the post-disaster recovery is specifically:

the unmanned aerial vehicle goes to a target area and builds a temporary communication network, and EV state information and system fault conditions in the area are uploaded to a power distribution main station through an unmanned aerial vehicle base station;

the distribution main station gives a scheduling instruction to guide the EV to enter a target V2G site, and simultaneously controls the action of the telemechanical switch to form a micro-grid centered on the V2G site, so that the power supply recovery of the ground communication base station is ensured, and the load is picked up to the maximum extent;

After the ground communication base station of the original area normally operates, the unmanned aerial vehicle goes to the next area to continue to be unfolded and restored, and the micro-grid topology dynamically changes along with the restoration of the communication of the new area until the whole distribution information physical system is restored.

In a second aspect, an embodiment of the present invention provides an elastic power distribution network information physical collaboration fast recovery system, including:

the scheduling module is used for establishing an EV pre-disaster scheduling model;

the pre-disaster module is used for establishing a pre-disaster stage preparation model based on the EV pre-disaster scheduling model obtained by the scheduling module so as to realize pre-disaster preparation;

the communication module is used for determining the communication coverage of the unmanned aerial vehicle based on the suspension position of the unmanned aerial vehicle and environmental factors, and simultaneously considering that a telemechanical switch and a V2G site in a recovery area are covered by communication to establish a regional unmanned aerial vehicle communication demand model;

the post-disaster module is used for determining EV refuge results based on the regional unmanned aerial vehicle communication demand model obtained by the pre-disaster module and the pre-disaster stage preparation model obtained by the communication module, and establishing a post-disaster stage dynamic recovery model with the minimum system weight loss load of the whole recovery period as a target;

and the recovery module is used for acquiring the fault scene information of the power distribution network based on the post-disaster stage dynamic recovery model obtained by the post-disaster module, so as to realize post-disaster recovery.

Compared with the prior art, the invention has at least the following beneficial effects:

according to the information physical collaboration quick recovery method of the elastic power distribution network, a pre-disaster and post-disaster stage flexible resource allocation scheme is designed according to the characteristics of flexible resources of an information layer and a physical layer, and the elasticity of the power distribution network can be effectively improved.

Furthermore, the EV is guided to participate in the refuge scheduling process through the EV pre-disaster scheduling model, so that the personal safety of the user is ensured, and the EV conditions of all refuge stations before disaster are counted more accurately.

Further, the driving characteristics of the EV are described through travel chains and real-time state constraints, and travel willingness coefficients are introduced to quantify the proportion of EV users participating in refuge scheduling.

Furthermore, unmanned aerial vehicle emergency communication requirements and scheduling behaviors of the EV in each area in the pre-disaster preparation stage are modeled, and preparation work is prepared for quick recovery after disaster of the system. In order to ensure reasonable dispatch of unmanned aerial vehicles after a disaster, the minimum number of unmanned aerial vehicles required for establishing an emergency communication network in each area needs to be determined. In addition, the EV refuge scheduling process can be characterized through the EV pre-disaster scheduling model and corresponding constraint, and then EV conditions of all refuge stations before disaster are obtained.

Further, the minimum demand of each regional communication unmanned aerial vehicle is determined through the regional unmanned aerial vehicle communication demand model, and then a post-disaster unmanned aerial vehicle allocation scheme is determined.

Further, the post-disaster stage dynamic recovery model models the scheduling process, the micro-grid topology and the operation characteristics of the unmanned aerial vehicle and the EV so as to realize the rapid recovery of the information physical system of the power distribution network.

Furthermore, scheduling and matching of flexible resources after disaster are constrained through information physical coupling constraint, so that the flexible resources meet the information physical coupling characteristic.

Furthermore, unmanned aerial vehicle scheduling, EV scheduling and power distribution network operation meet basic physical relations through unmanned aerial vehicle scheduling constraint, EV scheduling constraint and power distribution network operation constraint.

Further, after-disaster stage decisions are made to obtain flexible resource operation strategies, the power distribution network is guided to recover, and power failure loss is reduced to the maximum extent.

It will be appreciated that the advantages of the second aspect may be found in the relevant description of the first aspect, and will not be described in detail herein.

In summary, a preparation model is established in the pre-disaster stage, EV in the pre-disaster broadcast disaster information guiding system participates in refuge scheduling, and meanwhile, an emergency central unmanned aerial vehicle is in emergency waiting order, so that preparation is made for rapid recovery of a post-disaster power distribution network information physical system; and a power distribution network dynamic recovery model which considers the reconstruction time-space collaborative optimization of the unmanned aerial vehicle, the electric automobile V2G and the telemechanical switch network carrying the emergency communication module is constructed at the post-disaster stage, so that an effective scheme is provided for the scheduling of the unmanned aerial vehicle and the cluster EV. According to the method, the flexible resource association relation between the information layer and the physical layer under the information physical coupling characteristic is fully researched, the information physical system of the power distribution network is quickly and efficiently recovered, and the power failure loss is reduced to the maximum extent.

The technical scheme of the invention is further described in detail through the drawings and the embodiments.

Drawings

FIG. 1 is a schematic diagram of a communication-power-traffic coupling system;

FIG. 2 is a flow chart for power distribution information physical system recovery;

fig. 3 is a schematic diagram of a computer device according to an embodiment of the invention.

Fig. 4 is a block diagram of a chip according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the description of the present invention, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.

It should be understood that although the terms first, second, third, etc. may be used to describe the preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish one preset range from another. For example, a first preset range may also be referred to as a second preset range, and similarly, a second preset range may also be referred to as a first preset range without departing from the scope of embodiments of the present invention.

Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.

The invention provides an information physical collaboration quick recovery method of an elastic power distribution network, which establishes a pre-disaster stage preparation model and a post-disaster stage dynamic recovery model according to the self characteristics of flexible resources of an information layer and a physical layer and the recovery requirements of a post-disaster communication network and a power grid, designs a flexible resource allocation scheme of the pre-disaster stage and the post-disaster stage according to the self characteristics of the flexible resources of the information layer and the physical layer, and can effectively improve the elasticity of the power distribution network. The unmanned aerial vehicle can be flexibly deployed and quickly built into a communication network according to requirements after a disaster by virtue of excellent movement characteristics, and the cluster EV has energy space-time transfer capability and can provide reliable power support for important loads after the disaster, however, the research at present usually ignores the influence of communication failure on EV scheduling, does not explore the potential of EV resources to participate in the physical recovery of information of a power distribution network, does not fully consider the coordination and coordination of flexible resources of an information layer and a physical layer, and cannot deeply embody the beneficial effect of information physical coordination on the elasticity of the power distribution network. According to the method, a preparation model is established in a pre-disaster stage, EV in a pre-disaster broadcast disaster information guiding system participates in refuge scheduling, and meanwhile, an emergency center unmanned aerial vehicle is in emergency waiting order, so that preparation is made for quick recovery of a post-disaster power distribution network information physical system; and a power distribution network dynamic recovery model which considers the reconstruction time-space collaborative optimization of the unmanned aerial vehicle, the electric automobile V2G and the telemechanical switch network carrying the emergency communication module is constructed at the post-disaster stage, so that an effective scheme is provided for the scheduling of the unmanned aerial vehicle and the cluster EV. And the flexible resource association relation between the information layer and the physical layer under the information physical coupling characteristic is fully researched, the information physical system of the power distribution network is quickly and efficiently recovered, and the power failure loss is reduced to the maximum extent.

The invention discloses a method for quickly recovering information physical collaboration of an elastic power distribution network, which comprises four stages of researching a system, recovering a flow, establishing a pre-disaster stage preparation model and establishing a post-disaster stage dynamic recovery model, and comprises the following steps:

s1, establishing an EV pre-disaster scheduling model;

constraint conditions comprise traffic network constraint, EV travel chain constraint, EV state constraint and EV response willingness constraint, so that the EV quantity ratio of participating response is counted, and meanwhile EV scheduling results of all refuge stations are obtained.

Referring to fig. 1, research is developed based on a power distribution network information physical system and an intelligent transportation system (intelligent transportation system, ITS), the two systems are integrated into a communication-electric-transportation system, and the power distribution network information layer includes all communication devices, software, protocols, topology structures and the like adopted by a communication network; the physical layer contains power primary devices and various types of distributed power sources.

The power distribution network communication system considered by the invention adopts a wireless communication mode and a system architecture of a master station and a slave station. The communication network is divided into two parts, a main communication network is arranged between the power distribution main station and the sub-stations, and a ring network structure of a multi-service transmission platform (multi-service transfer platform, MSTP) is adopted, so that the reliability is high; the communication network is connected between the distribution electronic station and the terminal equipment, and a wireless communication mode is adopted, so that the communication base station after disaster can not transmit the power grid state information collected by the terminal equipment and the control instruction issued by the master station due to power failure.

The ITS can obtain various real-time traffic information and vehicle position data by integrating advanced technologies such as a geographic information system (geographic information system, GIS) and a global positioning system (global positioning system, GPS). The terminal electronic device mounted on the EV can monitor and collect EV real-time status information, and the driver can easily acquire external information including a navigation plan through the wireless network. Once communication resumes after the disaster, the distribution master station can be used as an information processing center to acquire information such as the charge state and the space position of the EV in the system.

The EV pre-disaster scheduling model is specifically as follows:

road network model:

G＝(V,E) (6)

v is a node set in the road network; e is a set of connection relations of all nodes in the road network, the matrix D (G) represents the distance between all the nodes in the road network, and the shortest running path between any two nodes can be obtained by using Dijkstra algorithm.

EV pass chain model:

Chain＝{B ₀ ,B _f ,W _0f ,L _0f ,T ₀ ,T _f ,T _0f ,T _p } (7)

wherein, chain is travel Chain set; the collection includes the starting point B of the travel chain ₀ End point B of travel chain _f Travel chain line W _0f Length L of line _0f Departure time T ₀ Time of arrival T _f Driving time T _0f And residence time T _p 。

T _f,n ＝T _0,n +T _0f,n (9)

T _0,n+1 ＝T _f,n +T _p,n (10)

Wherein d _n Is the length of the nth segment of line; v' is the EV average driving speed.

EV state model:

EVstatus＝{Cap _rem,t ,w _t ,P _t } (11)

the EVstatus is a set of EV real-time status information, and comprises the current residual electric quantity of the EV, the running status and the number of the V2G station closest to the current EV.

EV response ratio model:

N _res ＝ρN _total (12)

wherein N is _res EV number for participation in refuge response; n (N) _total The number of EVs in the system is all.

S2, establishing a pre-disaster stage preparation model based on EV scheduling;

and (3) finishing the emergency standby process of the pre-disaster EV scheduling and emergency center unmanned aerial vehicle, and preparing for the physical and rapid recovery of the post-disaster power distribution network information.

Referring to fig. 2, after-disaster communication recovery is achieved by deploying an unmanned aerial vehicle, and then an EV is guided to a V2G site to form a distributed power supply (distributed generator, DG) to recover power supplied by a base station and a telemechanical switch, so that a power distribution network is controlled to form a plurality of micro-grids centering on DG, and more load recovery is achieved.

The preparation before disaster is specifically as follows:

dividing a power distribution network into a plurality of areas according to the communication coverage range of a ground base station, wherein all telemechanical switches in each area are provided with communication support by the same base station in a normal state;

modeling the emergency communication requirement of the unmanned aerial vehicle after each regional disaster, and realizing the full coverage of the regional communication network by using the minimum unmanned aerial vehicle number;

Finally, the ITS broadcasts disaster information in advance, the EV running in the urban road stops free dispatching and goes to the nearest refuge site for refuge, and unmanned aerial vehicle equipment managed by the emergency command center enters an emergency standby state, so that preparation capable of quickly and effectively responding is prepared.

Unmanned aerial vehicle emergency communication requirements and scheduling behaviors of EV in each area of the pre-disaster stage are modeled, and preparation work is made for rapid recovery after the disaster of the system. In order to ensure reasonable dispatch of unmanned aerial vehicles after a disaster, the minimum number of unmanned aerial vehicles required for establishing an emergency communication network in each area needs to be determined. In addition, the traveling characteristics of the EV are described through a traveling chain and a real-time state model, and traveling willingness coefficients are introduced to quantify the proportion of EV users participating in refuge scheduling, so that the EV conditions of each refuge station before disaster can be counted more accurately.

S3, establishing a regional unmanned aerial vehicle communication demand model;

determining the communication coverage of the unmanned aerial vehicle based on the suspension position of the unmanned aerial vehicle and environmental factors, and simultaneously considering that a telemechanical switch and a V2G site in a recovery area are provided with communication coverage, and establishing an area unmanned aerial vehicle communication demand model so as to determine the minimum demand quantity of unmanned aerial vehicles in each area; the model is further discretized, so that the construction and the solving of the model are easier to realize.

Regional unmanned aerial vehicle communication demand model

The minimum unmanned aerial vehicle demand of each area needs to be calculated, the unmanned aerial vehicle communication coverage range is related to environmental factors, the problem can be modeled into a discretization model, the minimum unmanned aerial vehicle suspension working point is determined under the condition that all bus node telemechanical switching devices and V2G sites of the area are covered, so that the minimum unmanned aerial vehicle demand of each area is defined, and the model is as follows:

s.t.

wherein p is _m Is 0-1 variable, p if point m is selected as the unmanned aerial vehicle operating point _m =1, otherwise p _m ＝0；h _i,m A 0-1 variable, indicating whether the communication node i is covered by the unmanned aerial vehicle at the point m, if so _i,m =1, otherwise h _i,m ＝0；d _i,m Is the straight line distance at communication node i and point m; x and y respectively represent the abscissa and the ordinate of the node; c is a set of communication nodes; m is the set of unmanned operating points. Equation (2) represents that each communication node i is covered by at least one unmanned aerial vehicle; equations (4) and (5) represent that the communication node can be covered by the drone only if the drone is less than the radius of the communication circle from the communication node.

S4, establishing a post-disaster stage dynamic recovery model by taking the minimum weighted load loss of the system in the whole recovery period as a target based on the EV refuge result obtained by the regional unmanned aerial vehicle communication requirement and the pre-disaster stage preparation model;

Real-time scheduling is carried out on the information layer flexible resource unmanned plane, the physical layer flexible resource EV and the telemechanical switch, and constraint conditions comprise unmanned plane scheduling constraint, EV scheduling constraint, radial topology constraint of network reconstruction, power distribution network operation constraint and information physical coupling constraint, so that the information physical quick recovery of the power distribution network is realized, and the power failure loss is reduced to the greatest extent.

The post-disaster stage dynamic recovery model is specifically as follows:

and modeling a scheduling process, a micro-grid topology and operation characteristics of the unmanned aerial vehicle and the EV in a post-disaster stage so as to realize quick recovery of the information physical system of the power distribution network. The unmanned aerial vehicle mainly bears the tasks of guiding EV rescheduling to form a distributed power supply and controlling a tele-switch to close to form a micro-grid and guaranteeing communication between a ground base station and the distributed power supply, and once the communication function of the ground base station is recovered to be normal, the unmanned aerial vehicle can be dispatched to go to the next area.

(1) Objective function

Where m is the current period; t is the total number of time periods involved in recovering the full cycle; b is a distribution network node set; omega _i Is the importance of the load connected with the ith node;a 0-1 variable representing whether the load of the ith node is accessed in the t-th period;is the load amount of the ith node of the t-th period.

(2) Unmanned aerial vehicle dispatch model

Spatial constraint:

/>

wherein x is _i,j,c 0-1 variable representing the c-th unmanned plane from the working point i to j; u is a set of all unmanned aerial vehicles; m is a set of working points; s and R respectively represent a starting point set and a final point set of the unmanned aerial vehicle; y is _i,c Is 0-1 variable, if the communication base station is recovered to be normal when the c unmanned aerial vehicle leaves the i power-losing communication base station, the variable value is 1; n is a set of powered down communication base stations; mat (Mat) _i Is the capacity required by the ith EV transmission data;is the communication capacity of the drone c. The formula (14) shows that the working route of the unmanned aerial vehicle is unidirectional, and the unmanned aerial vehicle can reach the next power-off base station after leaving the last power-off base station; equation (15) shows that the unmanned aerial vehicle starts from the starting point to the working point and the end point in a single direction, and the unmanned aerial vehicle is not allowed to turn back in the middle; equation (16) represents that the unmanned aerial vehicle cannot repeatedly go to the same working point; formulas (17) and (18) are power restoration completion constraints of the power-off base station; formula (19) represents an unmanned aerial vehicleThe number of EVs that can be accommodated is limited in the case where the transmission data capacity of the machine is fixed.

Time constraint:

wherein,the moment when the unmanned plane c reaches the working point i; />Is the time that the unmanned aerial vehicle c stays at the working point i;is the transit time of the unmanned aerial vehicle c from the working point i to the working point j. Equation (20) indicates that when the unmanned aerial vehicle arrives at the operating point j from the operating point i, the time when the unmanned aerial vehicle arrives at the operating point j is equal to or greater than the time when the unmanned aerial vehicle arrives at the operating point i plus the suspension time and the movement time between the two.

(3) EV scheduling model

Space-time constraint:

wherein,is a 0-1 variable, if the jth EV arrives at the ith V2G site in the t-period +.>Otherwise-> Rescheduling the ith EV for consuming time in the road traffic network, wherein the time can be determined by Dijkstra algorithm; n (N) _V2G Representing the number of V2G sites; e is an electric automobile set; v is a V2G site set; t is the recovery full period set. Equation (21) indicates that each EV can only enter one V2G site at most in the rescheduling process; equation (22) indicates that the EV can interact with the grid after arriving at the V2G site; equation (23) indicates that the location is not changed after the EV enters the target V2G site, i.e., the problem of multiple scheduling of the EV is not considered due to the influence of economy and urgency of scheduling.

Number constraint:

wherein N is _EV Indicating the number of EVs involved in rescheduling; n (N) _i,VE Is the number of EVs that the ith V2G site can accommodate the maximum. The meaning of equation (24) is that the number of EVs scheduled into a V2G site should not be greater than the total number of EVs involved in rescheduling; the meaning of equation (25) is that the number of EVs scheduled into a V2G site should not exceed the maximum number of EVs that the V2G site can accommodate.

(4) Dynamic recovery model of power distribution network

Topology constraints:

-M·d _k,t ≤f _k,t ≤M·d _k,t ,k∈L,t∈T (26)

-M·(1-d _k,t )+f _j,t ≤f _i,t ≤M·(1-d _k,t )+f _j,t (27)

wherein d _k,t Is 0-1 variable, is the switching decision of the master station on the kth line, and d is the control line is closed _k,t =1, otherwise d _k,t ＝0；f _k,t A commodity stream representing a kth line; f (f) _i,t Commodity flow requirements representing an ith node;commodity flow injection quantity representing power supply nodes; d (D) _k,i Is a 0-1 variable representing whether the kth line is connected to the ith node; l is a line set; g is a set of power supply nodes; f (F) _i Is the set of lines connected to the i-th node. Equations (26) - (30) are single commodity flow constraints that ensure radial topology and internal node connectivity of the microgrid, and a single microgrid is powered by only one distributed power source.

Operation constraint:

p _k,t ≤p _k,max (31)

q _k,t ≤q _k,max (32)

-M·d _k,t ≤p _k,t ≤M·d _k,t ,k∈L,t∈T (33)

-M·d _k,t ≤q _k,t ≤M·d _k,t ,k∈L,t∈T (34)

/>

0.9V _ref ≤V _i,t ≤1.1V _ref s (39)

wherein p is _k,t And q _k,t Respectively representing the active power and the reactive power of the kth line;and->Representing the active and reactive load demands of the ith node, respectively; />And->Active and reactive injection representing the ith power supply node, respectively; />A 0-1 variable for indicating whether the load of the ith node of the table is accessed; />And->Representing the maximum and minimum power of the V2G site to the grid. Formulas (31) - (34) are line power constraints; formulas (35) - (38) are node power balancing constraints; equation (39) is the node voltage magnitude constraint; equation (40) is the adjacent node voltage constraint; formula (41) is a load access constraint; equation (42) is the V2G site output constraint.

Firstly, if an originally closed circuit is to be disconnected, at least one communication node at two ends of the originally closed circuit needs to be controlled by a main station;

And if an originally opened line is to be closed, the communication nodes at the two ends of the originally opened line are communicated with the master station normally.

The information physical coupling constraint is specifically as follows:

wherein,is a 0-1 variable of the initial switch state of the kth line.

S5, acquiring fault scene information of the power distribution network.

The fault condition and the electrical parameter are preset for the example system, and the recovery area is divided according to the power failure condition of the communication base station, so that the optimal recovery strategy is obtained through subsequent simulation optimization

The recovery after disaster is specifically as follows:

the unmanned aerial vehicle goes to a target area and builds a temporary communication network, and EV state information and system fault conditions in the area can be uploaded to a power distribution main station through an unmanned aerial vehicle base station;

then, the distribution main station issues a scheduling instruction to guide the EV to drive into a target V2G site, and simultaneously controls a telemechanical switch to act to form a micro-grid centered on the V2G site, so that the power supply recovery of the ground communication base station is ensured, and the load is picked up to the maximum extent;

and finally, after the ground communication base station of the original area normally operates, the unmanned aerial vehicle goes to the next area to continue to be unfolded and restored, and the micro-grid topology dynamically changes along with the restoration of the communication of the new area until the whole distribution information physical system is restored.

In still another embodiment of the present invention, an elastic power distribution network information physical collaboration fast recovery system is provided, where the system can be used to implement the above elastic power distribution network information physical collaboration fast recovery method, and specifically, the elastic power distribution network information physical collaboration fast recovery system includes a scheduling module, a pre-disaster module, a communication module, a post-disaster module, and a recovery module.

In yet another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor of the embodiment of the invention can be used for the operation of the elastic power distribution network information physical collaboration quick recovery method, and comprises the following steps:

Establishing an EV pre-disaster scheduling model; establishing a pre-disaster stage preparation model based on the EV pre-disaster scheduling model to realize pre-disaster preparation; determining the communication coverage of the unmanned aerial vehicle based on the suspension position of the unmanned aerial vehicle and environmental factors, and simultaneously considering that a telemechanical switch and a V2G site in a recovery area are covered by communication to establish a regional unmanned aerial vehicle communication demand model; determining EV refuge results based on the regional unmanned aerial vehicle communication demand model and the pre-disaster stage preparation model, and establishing a post-disaster stage dynamic recovery model with the minimum of the system weighted load loss of the whole recovery period as a target; and acquiring the fault scene information of the power distribution network based on the post-disaster stage dynamic recovery model, and realizing post-disaster recovery.

Referring to fig. 3, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, the computer program 63 when executed by the processor 61 implements the reservoir inversion wellbore fluid composition calculation method of the embodiment, and is not described in detail herein to avoid repetition. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of each model/unit in the embodiment of the fast recovery system for information physical collaboration of the elastic distribution network, which is not described herein in detail for avoiding repetition.

The computer device 60 may be a desktop computer, a notebook computer, a palm top computer, a cloud server, or the like. Computer device 60 may include, but is not limited to, a processor 61, a memory 62. It will be appreciated by those skilled in the art that fig. 3 is merely an example of a computer device 60 and is not intended to limit the computer device 60, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a computer device may also include an input-output device, a network access device, a bus, etc.

The processor 61 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk provided on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like.

Further, the memory 62 may also include both internal storage units and external storage devices of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

Referring to fig. 4, the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more in number, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in memory 632 may include one or more modules each corresponding to a set of instructions. Further, the processor 622 may be configured to execute the computer program to perform the resilient power distribution network information physical collaboration quick recovery method described above.

In addition, chip 600 may further include a power supply component 626 and a communication component 650, where power supply component 626 may be configured to perform power management of chip 600, and communication component 650 may be configured to enable communication of chip 600, e.g., wired or wireless communication. In addition, the chip 600 may also include an input/output (I/O) interface 658. Chip 600 may operate based on an operating system stored in memory 632.

In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM Memory or a Non-Volatile Memory (Non-Volatile Memory), such as at least one magnetic disk Memory.

One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for physically and cooperatively recovering information about an elastic distribution network in the above embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:

In summary, according to the method and the system for the information physical collaboration rapid recovery of the elastic power distribution network, a preparation model is established in the pre-disaster stage, the EV in the disaster information guiding system is involved in evacuation scheduling through pre-disaster broadcasting, and meanwhile, an emergency central unmanned aerial vehicle is in emergency waiting order, so that preparation is made for rapid recovery of the information physical system of the power distribution network after disaster; and a power distribution network dynamic recovery model which considers the reconstruction time-space collaborative optimization of the unmanned aerial vehicle, the electric automobile V2G and the telemechanical switch network carrying the emergency communication module is constructed at the post-disaster stage, so that an effective scheme is provided for the scheduling of the unmanned aerial vehicle and the cluster EV. According to the invention, the flexible resource association relation between the information layer and the physical layer under the information physical coupling characteristic is fully researched, the information physical system of the power distribution network is quickly and efficiently recovered, and the power failure loss is reduced to the maximum extent; according to the self characteristics of flexible resources of the information layer and the physical layer, a flexible resource allocation scheme in the pre-disaster stage and the post-disaster stage is designed, and the elasticity of the power distribution network can be effectively improved.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random-Access Memory (RAM), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, etc., it should be noted that the content of the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions, such as in some jurisdictions, according to the legislation and patent practice, the computer readable medium does not include electrical carrier wave signals and telecommunications signals.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims

1. The method for quickly recovering the information physical collaboration of the elastic power distribution network is characterized by comprising the following steps of:

s1, establishing an EV pre-disaster scheduling model;

2. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 1, wherein the pre-EV disaster scheduling model is specifically:

road network model:

G＝(V,E)

EV pass chain model:

Chain＝{B ₀ ,B _f ,W _0f ,L _0f ,T ₀ ,T _f ,T _0f ,T _p }

T _f,n ＝T _0,n +T _0f,n

T _0,n+1 ＝T _f,n +T _p,n

wherein T is _0f,n T for driving EV on the nth stage route _f,n For EV reaching the end time of the nth section line, T _0,n+1 D is the departure time of the EV n+1th section line _n Is the length of the nth segment of line; v' is EV average drivingA speed;

EV state model:

EVstatus＝{Cap _rem,t ,w _t ,P _t }

EV response ratio model:

N _res ＝ρN _total

3. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 2, wherein the constraint conditions of the scheduling model before the EV disaster comprise traffic network constraint, EV travel chain constraint, EV state constraint and EV response willingness constraint.

4. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 1, wherein the pre-disaster preparation is specifically as follows:

5. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 1, wherein the regional unmanned aerial vehicle communication demand model is as follows:

s.t.

6. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 1, wherein the post-disaster stage dynamic recovery model is specifically:

objective function

Where m is the current period; t is the total number of time periods involved in recovering the full cycle; b is a distribution network node set; omega _i Is the importance of the load connected with the ith node;a 0-1 variable representing whether the load of the ith node is accessed in the t-th period; />Is the load amount of the ith node in the t-th period;

unmanned aerial vehicle dispatch model

Spatial constraint:

time constraint:

wherein,the moment when the unmanned plane c reaches the working point i; />Is the time that the unmanned aerial vehicle c stays at the working point i; />The transit time of the unmanned aerial vehicle c from the working point i to the working point j;

EV scheduling model

Space-time constraint:

wherein,is a variable from 0 to 1; t (T) _i ^travel Rescheduling time consuming in the road traffic network for the ith EV; n (N) _V2G Representing the number of V2G sites; e is an electric automobile set; v is a V2G site set; t is a recovery full period set;

number constraint:

dynamic recovery model of power distribution network

Topology constraints:

-M·d _k,t ≤f _k,t ≤M·d _k,t ,k∈L,t∈T

-M·(1-d _k,t )+f _j,t ≤f _i,t ≤M·(1-d _k,t )+f _j,t

operation constraint:

p _k,t ≤p _k,max

q _k,t ≤q _k,max

-M·d _k,t ≤p _k,t ≤M·d _k,t ,k∈L,t∈T

-M·d _k,t ≤q _k,t ≤M·d _k,t ,k∈L,t∈T

0.9V _ref ≤V _i,t ≤1.1V _ref s

wherein p is _k,t And q _k,t Respectively representing the active power and the reactive power of the kth line; And->Representing the active and reactive load demands of the ith node, respectively; />And->Active and reactive injection representing the ith power supply node, respectively; />A 0-1 variable for indicating whether the load of the ith node of the table is accessed; />And->Representing the maximum and minimum power of the V2G site to the grid.

7. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 6, wherein when an originally closed circuit is disconnected, at least one communication node at two ends of the originally closed circuit needs to be controlled by a host station; when an originally opened line is closed, communication nodes at two ends of the originally opened line are communicated with a master station normally; the information physical coupling constraint is specifically as follows:

8. The method for collaborative and rapid recovery of information physics of an elastic power distribution network according to claim 6, wherein the constraints of the post-disaster stage dynamic recovery model include unmanned aerial vehicle scheduling constraints, EV scheduling constraints, network-reconstructed radial topology constraints, power distribution network operation constraints, and information physical coupling constraints.

9. The method for quickly recovering information physical collaboration of an elastic power distribution network according to claim 1, wherein the post-disaster recovery is specifically:

10. The utility model provides an elasticity distribution network information physics cooperatees quick recovery system which characterized in that includes: