CN116031918A - Dynamic elastic power supply recovery method and system for urban power system under typhoon disaster - Google Patents
Dynamic elastic power supply recovery method and system for urban power system under typhoon disaster Download PDFInfo
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Abstract
The invention discloses a dynamic elastic power supply recovery method of an urban power system under typhoon disasters, which comprises the steps that a V2G system performs an operation period and collects road network EV traffic data during disaster early warning; providing electric energy support for the urban power grid by adopting EV resources at a temporary V2G interaction station in the disaster, and dispatching maintenance teams according to traffic data to carry out rush repair operation on a fault line; the maintenance team updates the rush-repair scheme in real time by adopting an optimized scheduling method considering the V2G data and repairs the next fault equipment; repeating the steps until all the fault devices are salvaged. The invention also discloses a system for realizing the dynamic elastic power supply recovery method of the urban power system under typhoon disasters. The invention is beneficial to realizing the quick recovery of the post-disaster system and improving the elasticity of the system; the invention has high reliability, good accuracy and higher efficiency.
Description
Technical Field
The invention belongs to the field of safe operation of power systems, and particularly relates to a dynamic elastic power supply recovery method and system for an urban power system under typhoon disasters.
Background
Along with the development of economic technology and the improvement of living standard of people, electric energy becomes an indispensable secondary energy source in the production and living of people, and brings endless convenience to the production and living of people. Therefore, ensuring stable and reliable supply of electric energy becomes one of the most important tasks of the electric power system.
With the development of power electronics technology and new energy, electric Vehicles (EV) are widely used. Through V2G (V2G) technology, EVs have the ability to participate in urban power system power restoration after a disaster. Compared with the traditional mobile power supply, the EV is used as a common traffic carrier, and has the advantages of large quantity, wide distribution, rich resources and flexible scheduling. The V2G interaction station is used as a coupling node of a traffic network and an urban power system, can effectively correlate the traffic network and the urban power system data, provides reliable traffic data for manual rush repair, and effectively improves the manual rush repair efficiency.
However, in disaster situations such as typhoons, the probability of traffic accidents in urban road networks is increased, traffic interruption can be caused after the towers, trees and the like along the lines are damaged, and the factors are needed to be considered in the manual rush-repair process. When the problems are solved, the V2G system has the natural advantages of road network and power grid coupling. Therefore, the road network information acquisition function and the recovery function of the V2G can bring great help to travel decisions, path decisions and the like of emergency repair personnel.
However, at present, no technical scheme exists for participating in the rush repair of the urban power system under the typhoon disaster by the V2G system. The situation can definitely greatly delay the repair progress of the urban power system under the typhoon disaster, and simultaneously greatly reduce the reliability of the power system.
Disclosure of Invention
The invention aims to provide a dynamic elastic power supply recovery method for an urban power system under typhoon disasters, which has high reliability, good accuracy and higher efficiency.
The second purpose of the invention is to provide a system for realizing the dynamic elastic power supply recovery method of the urban power system under typhoon disasters.
The invention provides a dynamic elastic power supply recovery method for an urban power system under typhoon disasters, which comprises the following steps:
s1, during disaster early warning, a V2G system performs an operation period and collects road network EV traffic data;
s2, when a disaster comes, the V2G interaction station adopts EV resources to provide electric energy support for the urban power grid, and dispatches maintenance teams to carry out rush repair operation on the fault line according to traffic data;
s3, before repairing the next fault equipment, a maintenance team updates the first-aid repair scheme in real time by adopting an optimized scheduling method considering V2G data, and repairs the next fault equipment according to the updated first-aid repair scheme;
S4, repeating the steps S2 to S3 until all the fault devices are salvaged.
The collecting road network EV traffic data in step S1 specifically includes the following steps:
modeling a traffic network as G= (V, E, A), wherein G is a traffic network node connection line set, V is a traffic node set, E is a traffic connection set, A is a traffic network adjacency matrix and is used for describing the connection distance between each node; based on the traffic network adjacency matrix A, a shortest path running scheme between any two points in the traffic network is obtained through a shortest path algorithm;
modeling of traffic network and power system coupling:
the coupling between the traffic network and the power system is equivalent to the connection between nodes in the network, and a 0-1 variable mathematical model is constructed to represent the coupling relation:
ξ＝{ε αβ ∈V α V β }
wherein xi is the collection of connecting lines between the traffic network and the power system; epsilon αβ The system comprises a coupling line set formed by a traffic network and a power system; v (V) α Is a traffic node set; v (V) β The method is a power grid node set;
for a 0-1 variable representing whether there is an interlayer node connection between the traffic network and the power system, if +.>
The coupling relation between the traffic network node i and the power grid node j is shown, if +.>
The traffic network node i and the power grid node j are not in coupling relation; / >
Is the ith node of the traffic network; />
Is the j node of the power grid;
travel chain modeling based on travel willingness:
dividing an urban traffic network into three areas of a residential area, a commercial area and a scenic area, and dividing nodes of the three areas into sets H, B and S; the daily travel path of the user is regarded as a travel chain and passes through 2 area nodes and 3 area nodes:
Link＝{D 0 ,D f ,R 0f ,L 0f ,t 0 ,t f ,T 0f ,t p }
link is a travel chain state variable set; d (D) 0 Is the starting point of the travel chain; d (D) f The travel chain end point; r is R 0f Is a travel path; l (L) 0f Is the path length; t is t 0 The travel time is the travel time; t is t f Is the arrival time; t (T) 0f Is the driving duration; t is t p Is the parking time length;
introducing a travel willingness coefficient rho to correct the total travel willingness change of the EV user for simulating the influence of the environment on the travel willingness of the user:
n up ＝ρn total
in n up EV number for willing travel; n is n total The total EV number;
the driving time T of the mth journey is expressed by the following formula 0f,m Time of arrival t f,m And the departure time t of the next journey 0,m+1 ：
t f,m ＝t 0 +T 0f,m
t 0,m+1 ＝t f,m +t p
D in m Length of the mth journey;
the average speed of the EV driving on the mth journey;
the departure time t of entering road network for the first time every day first (τ) is
Departure time t of last entering road network final (tau) is->
Wherein sigma is the standard deviation; mu is the average trip time; τ is more than 0 and less than or equal to 24;
EV real-time state modeling as
Wherein W is a set of real-time state information of each EV and is SOC a For the state of charge set of all EVs at time a, ψ a For the driving status set->
A V2G interaction station number set closest to the position where the moment a is located;
modeling the real-time state of the interaction station:
in the early period of disaster, the space-time state of the V2G interaction station is expressed as
V＝{SOV a ,P a ,λ a }
Wherein V is a set of respective real-time state information of each V2G interaction station at the time a; SOV (solid oxide Fuel cell) a The operation state set of all the V2G interaction stations at the moment a is set; p (P) a Is a rated capacity set; lambda (lambda) a Is a set of region coefficients.
The step S2 of dispatching a maintenance team to carry out the first-aid repair operation on the fault line according to the traffic data specifically comprises the following steps:
corresponding action time sequences are respectively established for typhoons, dispatching centers, V2G interaction stations and maintenance teams, and time periods are divided into four key nodes: t is t 0 The method comprises the steps that the model starting time is set time before disaster early warning; t is t 1 The disaster early warning time is the disaster early warning time; t is t 2 The moment when typhoon disasters affect the beginning is reached, and then the urban power system faults begin; t is t 3 At the moment of fault repair, the urban power system is recovered to be normal;
typhoon disasters are divided into four stages: a pre-disaster period, a mid-disaster period, a post-disaster period and a disaster recovery period; the urban power system is divided into a conventional period, a fault period and a conventional period according to the coming moment of typhoon disasters; the dispatching center is divided into a dormancy period, a response period, a dispatching period and a re-coiling period according to the early warning time and the coming time of the typhoon disaster; the V2G system is divided into: output period-operation period-input period-output period; the maintenance team is divided into a sleep period, a standby period, a rush repair period and a sleep period;
And confirming the path states among fault devices through the urban road network traffic data provided by the V2G, and adjusting the rush-repair scheme in real time according to traffic conditions.
The optimal scheduling method for considering the V2G data in the step S3 updates the rush-repair scheme in real time, and specifically comprises the following steps:
updating the state of the maintenance team according to different time periods:
the maintenance team is in a ready state during the standby period: the V2G interaction station enters an operation period to collect urban road network information and peripheral EV data and coordinate regional power system recovery to the dispatching center; after the disaster occurs, the moment when the urban power system is in fault is taken as a critical point between a standby period and a rush-repair period;
the maintenance team enters a rush-repair period: immediately starting to perform fault recovery on the urban power system by adopting a rolling time domain optimization method;
the maintenance team adopts a rolling time domain optimization method to schedule in the rush-repair period: the maintenance team repairs the line ij first; the time when the ith device is reached is 0 time when the device is repaired, and the time for repairing the device comprises repair time and time for going to the next device;
after the maintenance team reaches the ith fault equipment, judging whether the carried repair materials can meet the repair conditions of the equipment or not: if yes, repairing is carried out; if the current line is insufficient, reporting to an upper dispatching center and directly going to the next line for rush repair.
The rolling time domain optimization method specifically comprises the following steps:
the model aims at enabling the total load shedding amount of the power system to be minimum in a set time; the objective function is expressed as
Wherein h is the current moment; t is the number of time periods contained in the optimization time used; v is a node set; omega shape j The weight of the node load;
for load shedding power;
the set constraint conditions are as follows:
work angle constraint:
and->
In B of ij For line admittance, θ i,t For the phase angle of the line>
For line power, M is a set constant, u ij,t Is a variable of 0-1 and u ij,t =1 indicates that the line is in normal operation, u ij,t =0 indicates that the line is in an abnormal operation state, L is a transmission line set;
output constraint of the generator set:
wherein->
For the lower limit of the output power of the generator set, +.>
For the upper limit of the output power of the generator set, < >>
The output of the generator is output, and G is a generator set;
V2G force constraint:
wherein the method comprises the steps of
Lower limit of output power of V2G interaction station, < >>
For the upper limit of the output power of the V2G exchange station, < >>
The output of the interaction station is V2G, and V2G is a set of generator sets;
line capacity constraint:
wherein->
Maximum apparent power for line capacity; u (u) ij Is a variable of 0-1, u ij =1 indicates that the line is in a normal operation state at time t;
Node power balancing constraints:
wherein pi (j) is the parent node set of node j, delta (j) is the child node set of node j, +.>
For load demand, +.>
Is the load shedding amount;
load shedding amount constraint:
dispatch constraints for rush repair team:
wherein x is i,j,c The method comprises the steps that 0-1 variable from the fault equipment i to j of the c emergency repair team is used, EC is a set of all emergency repair teams, N is a set of the fault equipment, S is a starting point set of the emergency repair team, and R is a final point set of the emergency repair team; />
And (5) starting, finishing, entering and exiting constraint of the rush repair team:
the first constraint indicates that the rush-repair team must start from the starting point, the second constraint indicates that the rush-repair team must return to the end point, the third constraint indicates that the rush-repair team cannot go from the end point to the next fault device, and the fourth constraint indicates that the rush-repair team cannot go from the fault device to the starting point;
unrepeatable repair constraints: x is x i,i,c ＝0,
Wherein the constraint indicates that the rush repair team cannot leave a faulty device and reach the faulty device immediately;
whether the faulty device has been salvaged, using the variable y i,c Record, expressed as
Rush repair team leaving equipment record constraint:
wherein y is i,c For rush repair team leave record, y i,c A variable of 01, and if the fault equipment is recovered to be normal when the rush repair team leaves the fault equipment, the value is 1;
material carrying constraint: 
Wherein Bac i Materials required for repairing the faulty device i +.>
Carrying the total amount of materials for the rush repair team;
road network congestion constraints:
the method is characterized in that the road from the fault device i to the fault device j can not pass under the condition of traffic jam;
repair time constraint:
wherein->
Repair time length of fault device i for rush repair team c,/-for the fault device i>
For the length of the traffic of the rush repair team c from the faulty device i to the faulty device j, +.>
The moment from the fault device i to the fault device j is the first-aid repair team c; the moment when the rush repair team arrives at the fault device i from the starting point is defined as 0 moment,/o>
Time constraint of repair equipment for repairing faults of rush repair team is +.>
Wherein f i,t A variable of 0-1 is used for recording whether the fault equipment i is repaired completely, if the fault equipment i is repaired completely within a discrete period t, the value is 1, otherwise, 0 is taken;
repair times constraint of rush repair team:
each faulty device can be repaired at most once, expressed as
If the fault equipment i is not repaired, the moment when the rush repair team c reaches the equipment is 0, and the constraint expression is that
The device recovers the tidal current coupling constraint:
the fault equipment i is recovered to a normal state after repair is completed, the fault equipment state needs a variable record before and after repair, and the variable is expressed as:
wherein BB is a set of failed nodes; v i,t 01 variable which is the state of the equipment, and 1 if the equipment is repaired;
the line from the faulty device i to the device j can participate in operation after repair is completed, but the participation in tidal current operation after repair requires superior scheduling constraint expressed as
BL is a fault line set; u (u) ij,t The variable is 0-1 of whether the line participates in operation or not, and if the line participates in operation, the variable is 1;
if the faulty device has not been repaired, the line in which the faulty device is present is still considered faultyIf one device is still in a fault state on two sides of the line ij, the line is also in the fault state, and the expression is as follows: u (u) ij,t ≤v i, t v j,t ,
In specific implementation, mixed integer linear programming modeling is carried out, and the solution is carried out through a computer solver.
The invention also discloses a system for realizing the dynamic elastic power supply recovery method of the urban power system under typhoon disasters, which comprises a data collection module, a rush repair operation module, a scheduling module and an output module; the data collection module, the rush-repair operation module, the scheduling module and the output module are sequentially connected in series; the data collection module is used for carrying out an operation period on the V2G system during disaster early warning, collecting road network EV traffic data and uploading the data to the rush-repair operation module; the emergency repair operation module is used for temporarily providing electric energy support for the urban power grid by adopting EV resources at disaster according to the received data, dispatching a maintenance team to perform emergency repair operation on a fault line according to traffic data, and uploading the data to the scheduling module; the scheduling module is used for updating the first-aid repair scheme in real time by adopting an optimized scheduling method for considering V2G data by a maintenance team before repairing the next fault equipment according to the received data, repairing the next fault equipment according to the updated first-aid repair scheme, and transmitting the data out of the module and the first-aid repair operation module; the output module is used for repeating the rush repair operation module and the scheduling module until all the fault devices are completed in the rush repair process, and outputting the result.
The invention provides a dynamic elastic power supply recovery method and a system for an urban power system under typhoon disasters, and provides the dynamic elastic power supply recovery method for the urban power system under typhoon disasters, which is used for considering the repair uncertainty and V2G, establishing a dynamic recovery model for the urban power system under the coordination of V2G and rush repair team, facilitating the realization of the quick recovery of the system after the disaster and improving the system elasticity; the invention fully considers two factors of urban road network congestion and rush-repair environment, accurately models uncertainty in the repair process of the rush-repair team, optimizes the repair sequence of the rush-repair team on time sequence by using a rolling time domain optimization method, and has good practicability for the recovery of an elastic urban power system in the future; therefore, the invention has high reliability, good accuracy and higher efficiency.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a disaster timing model in the method of the present invention.
FIG. 3 is a schematic diagram of a rush repair team model in the method of the present invention.
FIG. 4 is a schematic diagram of an IEEE-39 node power system-30 node road network system in accordance with an embodiment of the present invention.
Fig. 5 is a schematic diagram of the output situation of the V2G charging pile after disaster occurrence according to an embodiment of the present invention.
FIG. 6 illustrates repair team repair paths for two cases of the master algorithm of the method embodiment of the present invention; (a) scheme 1 repair team repair path; (b) scheme 2 repair team repair path.
FIG. 7 illustrates repair team repair paths for two cases of a sub-example of an embodiment of the method of the present invention; (a) scheme 1 repair team repair path; (b) scheme 2 repair team repair path.
FIG. 8 is a schematic diagram of the system functional modules of the system of the present invention.
Detailed Description
A schematic process flow diagram of the method of the present invention is shown in fig. 1: the invention provides a dynamic elastic power supply recovery method for an urban power system under typhoon disasters, which comprises the following steps:
s1, during disaster early warning, a V2G system performs an operation period and collects road network EV traffic data; the method specifically comprises the following steps:
modeling a traffic network as G= (V, E, A), wherein G is a traffic network node connection line set, V is a traffic node set, E is a traffic connection set, A is a traffic network adjacency matrix and is used for describing the connection distance between each node; based on the traffic network adjacency matrix A, a shortest path running scheme between any two points in the traffic network is obtained through a shortest path algorithm;
modeling of traffic network and power system coupling:
The coupling between the traffic network and the power system is equivalent to the connection between nodes in the network, and a 0-1 variable mathematical model is constructed to represent the coupling relation:
ξ＝{ε αβ ∈V α V β }
wherein xi is the collection of connecting lines between the traffic network and the power system; epsilon αβ The system comprises a coupling line set formed by a traffic network and a power system; v (V) α Is a traffic node set; v (V) β The method is a power grid node set;
for a 0-1 variable representing whether there is an interlayer node connection between the traffic network and the power system, if +.>
The coupling relation between the traffic network node i and the power grid node j is shown, if +.>
The traffic network node i and the power grid node j are not in coupling relation; />
Is the ith node of the traffic network; />
Is the j node of the power grid;
travel chain modeling based on travel willingness:
dividing an urban traffic network into three areas of a residential area, a commercial area and a scenic area, and dividing nodes of the three areas into sets H, B and S; the daily travel path of the user is regarded as a travel chain and passes through 2 area nodes and 3 area nodes:
Link＝{D 0 ,D f ,R 0f ,L 0f ,t 0 ,t f ,T 0f ,t p }
link is a travel chain state variable set; d (D) 0 Is the starting point of the travel chain; d (D) f The travel chain end point; r is R 0f Is a travel path; l (L) 0f Is the path length; t is t 0 The travel time is the travel time; t is t f Is the arrival time; t (T) 0f Is the driving duration; t is t p Is the parking time length;
the driving trip in the disaster middle period after the typhoon disaster comes has a certain risk, which can have a certain influence on the trip decision of EV users, the adverse condition of roads under bad weather, the anxiety and panic emotion of unknown risks of the predicted disasters of the users can further reduce the trip willingness of the users; introducing a travel willingness coefficient rho to correct the total travel willingness change of the EV user for simulating the influence of the environment on the travel willingness of the user:
n up ＝ρn total
in n up EV number for willing travel; n is n total The total EV number;
the driving time T of the mth journey is expressed by the following formula 0f,m Time of arrival t f,m And the departure time t of the next journey 0,m+1 ：
t f,m ＝t 0 +T 0f,m
t 0,m+1 ＝t f,m +t p
D in m Length of the mth journey;
the average speed of the EV driving on the mth journey;
EV survey data shows that residents first and last dailyThe travel of the secondary entering road network has a certain rule and basically accords with normal distribution N (mu, sigma) 2 ) Wherein μ is the average travel time, σ 2 Is the variance; the departure time t of entering road network for the first time every day first (τ) is
Departure time t of last entering road network final (τ) is
Wherein sigma is the standard deviation; mu is the average trip time; τ is more than 0 and less than or equal to 24;
EV real-time state modeling as 
Wherein W is a set of real-time state information of each EV and is SOC a For the state of charge set of all EVs at time a, ψ a For the driving status set->
A V2G interaction station number set closest to the position where the moment a is located;
modeling the real-time state of the interaction station:
urban traffic network and power system nodes are uniformly distributed in three main areas in space and serve as power system nodes, and V2G interaction stations and the traffic network form a coupling relation; in the early period of disaster, the space-time state of the V2G interaction station is expressed as
V＝{SOV a ,P a ,λ a }
Wherein V is a set of respective real-time state information of each V2G interaction station at the time a; SOV (solid oxide Fuel cell) a The operation state set of all the V2G interaction stations at the moment a is set; p (P) a Is a rated capacity set; lambda (lambda) a Is a region coefficient set;
s2, when a disaster comes, the V2G interaction station adopts EV resources to provide electric energy support for the urban power grid, and dispatches maintenance teams to carry out rush repair operation on the fault line according to traffic data; the method specifically comprises the following steps:
corresponding action time sequences are respectively established for typhoons, dispatching centers, V2G interaction stations and maintenance teams, and time periods are divided into four key nodes: t is t 0 The method comprises the steps that the model starting time is set time before disaster early warning; t is t 1 The disaster early warning time is the disaster early warning time; t is t 2 The moment when typhoon disasters affect the beginning is reached, and then the urban power system faults begin; t is t 3 At the moment of fault repair, the urban power system is recovered to be normal; as shown in fig. 2;
typhoon disasters are divided into four stages: a pre-disaster period, a mid-disaster period, a post-disaster period and a disaster recovery period; the urban power system is divided into a conventional period, a fault period and a conventional period according to the coming moment of typhoon disasters; the dispatching center is divided into a dormancy period, a response period, a dispatching period and a re-coiling period according to the early warning time and the coming time of the typhoon disaster; the V2G system is divided into: output period-operation period-input period-output period; the maintenance team is divided into a sleep period, a standby period, a rush repair period and a sleep period;
the method comprises the steps that the path states among fault devices are confirmed through urban road network traffic data provided by V2G, and a rush-repair scheme is adjusted in real time according to traffic conditions;
s3, before repairing the next fault equipment, a maintenance team updates the first-aid repair scheme in real time by adopting an optimized scheduling method considering V2G data, and repairs the next fault equipment according to the updated first-aid repair scheme; the method specifically comprises the following steps:
updating the state of the maintenance team according to different time periods:
the maintenance team is in a ready state during the standby period: the V2G interaction station enters an operation period to collect urban road network information and peripheral EV data and coordinate regional power system recovery to the dispatching center; after the disaster occurs, the moment when the urban power system is in fault is taken as a critical point between a standby period and a rush-repair period;
The maintenance team enters a rush-repair period: immediately starting to perform fault recovery on the urban power system by adopting a rolling time domain optimization method;
the maintenance team adopts a rolling time domain optimization method to schedule in the rush-repair period: the maintenance team repairs the line ij first; the time when the ith device is reached is 0 time when the device is repaired, and the time for repairing the device comprises repair time and time for going to the next device;
after the maintenance team reaches the ith fault equipment, judging whether the carried repair materials can meet the repair conditions of the equipment or not: if yes, repairing is carried out; if the current line is insufficient, reporting to an upper dispatching center and directly going to the next line for rush repair;
in the specific implementation, due to certain uncertainty of the network condition of the urban road in the typhoon scene, traffic jam or road surface damage and other conditions possibly exist, the rush repair team is difficult to quickly reach the position of the fault equipment to recover power supply, and the duration of the rush repair team for the journey is difficult to estimate; in addition, in the repair process of the power system after actual disaster, the damage degree of each device is not fully mastered, and the repair time cannot be measured; therefore, a dynamic rush-repair team scheduling model based on rolling time domain optimization is considered to be designed, as shown in fig. 3; the rolling time domain optimization method specifically comprises the following steps:
The model aims at enabling the total load shedding amount of the power system to be minimum in a set time; the objective function is expressed as
Wherein h is the current moment; t is the number of time periods contained in the optimization time used; v is a node set; omega shape j The weight of the node load;
for load shedding power;
the set constraint conditions are as follows:
work angle constraint:
and->
In B of ij For line admittance, θ i,t For the phase angle of the line,/>
for line power, M is a set constant, u ij,t Is a variable of 0-1 and u ij,t =1 indicates that the line is in normal operation, u ij,t =0 indicates that the line is in an abnormal operation state, L is a transmission line set;
output constraint of the generator set: restraining the output of the generator set, wherein the output of the generator set is not lower than the lower limit and is not higher than the upper limit;
wherein->
For the lower limit of the output power of the generator set, +.>
For the upper limit of the output power of the generator set, < >>
The output of the generator is output, and G is a generator set;
V2G force constraint: constraining the output of the V2G interaction station, wherein the output of the V2G interaction station is not lower than a lower limit and is not higher than an upper limit;
wherein->
The lower limit of the output power of the V2G interaction station is P j V2G,max For the upper limit of the output power of the V2G exchange station, < >>
The output of the interaction station is V2G, and V2G is a set of generator sets;
Line capacity constraint: the capacity of the network line is limited, the constraint being that the value describing the actual transmission power of the line is notExceeding the maximum apparent power of the line capacity;
wherein the method comprises the steps of
Maximum apparent power for line capacity; u (u) ij Is a variable of 0-1, u ij =1 indicates that the line is in a normal operation state at time t;
node power balancing constraints: the power of each node is balanced;
wherein pi (j) is the parent node set of node j, delta (j) is the child node set of node j, +.>
For load demand, +.>
Is the load shedding amount;
load shedding amount constraint: amount of excision
Must not be higher than the load demand->
Dispatch constraints for rush repair team: the rush repair team is used as one of decision variables in the scheduling, and a mathematical model with reasonable numerical value and strict logic should be established. In a dynamic rush repair team model based on a rolling time domain, variables which directly influence scheduling decisions, such as equipment states, repair time, repair materials and the like, are required to be constrained; the maintenance line of the rush repair team does not have foldback,before the rush repair team reaches the fault equipment to perform rush repair, the rush repair team needs to leave from the last fault equipment, so that the basic constraint of the fault equipment access is designed as follows
Wherein x is i,j,c The method comprises the steps that 0-1 variable from the fault equipment i to j of the c emergency repair team is used, EC is a set of all emergency repair teams, N is a set of the fault equipment, S is a starting point set of the emergency repair team, and R is a final point set of the emergency repair team;
And (5) starting, finishing, entering and exiting constraint of the rush repair team: limiting departure and return logics of the rush repair team, wherein no repair record exists before departure of the starting point, and the action route is terminated after the starting point reaches the end point;
the first constraint indicates that the rush-repair team must start from the starting point, the second constraint indicates that the rush-repair team must return to the end point, the third constraint indicates that the rush-repair team cannot go from the end point to the next fault device, and the fourth constraint indicates that the rush-repair team cannot go from the fault device to the starting point;
unrepeatable repair constraints: the rush repair team can not leave a fault device and then immediately reach the fault device; x is x i,i,c ＝0,
i is epsilon N U S U R, wherein the constraint indicates that the rush-repair team can not leave a fault device and then reach the fault device immediately;
whether the faulty device has been salvaged, using the variable y i,c Record, expressed as
Rush repair team leaving equipment record constraint:
wherein y is i,c Record for leave of rush repair team，y i,c A variable of 01, and if the fault equipment is recovered to be normal when the rush repair team leaves the fault equipment, the value is 1;
material carrying constraint: the materials carried by the rush-repair team are limited, so that limitation constraint of the repair materials carried by the rush-repair team is set;
wherein Bac i Materials required for repairing the faulty device i +.>
Carrying the total amount of materials for the rush repair team;
Road network congestion constraints: based on the urban road network traffic condition information obtained by the V2G interaction station in the operation period, if congestion, interruption and the like exist on a road from the fault equipment i to the fault equipment j, the rush-repair team directly skips repairing the fault equipment j to the next line of the line ij. Constraints in the case of traffic congestion are expressed as;
the method is characterized in that the road from the fault device i to the fault device j can not pass under the condition of traffic jam;
repair time constraint: the arrival time, repair time, traffic time of the repair fault device i and the arrival time of the repair fault device j directly have constraint relation. The sum of the time for repairing the fault device i by the rush repair team and the traffic time from the fault device i to the fault device j is smaller than the moment of reaching the fault device j;
wherein->
Repair time length of fault device i for rush repair team c,/-for the fault device i>
For the length of traffic of the rush-repair team c from the faulty device i to the faulty device j,
the moment from the fault device i to the fault device j is the first-aid repair team c; the moment when the rush repair team arrives at the fault device i from the starting point is defined as 0 moment,/o>
The time constraint of the repair team fault repair equipment is as follows
Wherein f i,t A variable of 0-1 is used for recording whether the fault equipment i is repaired completely, if the fault equipment i is repaired completely within a discrete period t, the value is 1, otherwise, 0 is taken;
Repair times constraint of rush repair team:
each faulty device can be repaired at most once, expressed as
If the fault equipment i is not repaired, the moment when the rush repair team c reaches the equipment is 0, and the constraint expression is that
The device recovers the tidal current coupling constraint:
the fault equipment i is recovered to a normal state after repair is completed, the fault equipment state needs a variable record before and after repair, and the variable is expressed as:
wherein BB is a set of failed nodes; v i,t 01 variable which is the state of the equipment, and 1 if the equipment is repaired;
the line from the faulty device i to the device j can participate in operation after repair is completed, but the participation in tidal current operation after repair requires superior scheduling constraint expressed as
BL is a fault line set; u (u) ij,t The variable is 0-1 of whether the line participates in operation or not, and if the line participates in operation, the variable is 1;
if the faulty equipment is not repaired, the line with the faulty equipment still is regarded as faulty and cannot participate in operation, and if one of the two sides of the line ij is still in a faulty state, the line is also in the faulty state, and the expression is as follows: u (u) ij,t ≤v i, t v j,t ,
In specific implementation, mixed integer linear programming modeling is carried out, and a computer solver is used for solving;
s4, repeating the steps S2 to S3 until all the fault devices are salvaged.
The method of the invention is further described in connection with one embodiment as follows:
an IEEE-39 node power system-30 node road network system is employed to examine the proposed solution model. FIG. 4 illustrates the node location of the V2G charging pile; the loads are divided into primary, secondary and tertiary loads according to the importance degree, and the numbers of the nodes are shown in table 1; relevant parameters for EV are shown in Table 2.
TABLE 1 significance level classification schematic form
| Load class | Node numbering |
| First- |
3、4、5、11、14、24、39 |
| Two- |
2、6、7、10、15、23、29、38 |
| Three-stage load | Others |
Table 2 EV parameter schematic table
Assuming that the total EV number is 500 in the example, fig. 5 shows a case where the EV user takes part in the output of each V2G charging pile when traveling after the disaster occurs. Considering the influence of disasters on the trip will of EV users, the trip will of the users are taken in the scheme.
The IEEE-39 node system contains 10 generators and 46 transmission lines, and a maintenance warehouse is set near node 15. Now, it is assumed that the nodes 3, 10 and the lines 15-16, 10-13, 13-14, 25-37,2-30 fail due to disasters, and the approximate distances between the failure points and the warehouse and the failure points are shown in table 3. Table 4 lists the required service time and service resources for each failed node and line.
TABLE 3 schematic table of traffic distance (km) between faulty components and repair warehouse
TABLE 4 failure element repair time (h) and resource schematic Table for repair
In this example, the maintenance team schedule roll is optimized three times in total, so that the maintenance time of each element has three estimated values, which are separated by a slash. In order to meet the maintenance requirements of each fault element, the materials carried by each maintenance team in each scheme are 8 at most, and the time interval is set to be 0.5h. In order to facilitate the comparative analysis of the subsequent main calculation example and the sub calculation example, three schemes are designed as follows:
scheme 1: the V2G charging pile in the system does not participate in the power supply recovery process of the system, and power supply is recovered only through dispatching of a maintenance team;
scheme 2: the V2G charging pile in the system participates in the power supply recovery process of the system and cooperatively recovers power supply with a maintenance team;
scheme 3: and on the basis of the cooperative recovery of the V2G charging pile and the maintenance team, continuously updating the maintenance team scheduling scheme by using a rolling time domain optimization method.
Due to the actual calculation requirements, the solution first translates into maintenance team travel time based on the traffic distance in table 3. Assuming that the maintenance team is traveling at a constant speed, the maintenance team travel time can be directly derived from the distance and speed. Table 4 visually reflects the impact of the rolling optimization method on scheduling. In order to embody the uncertainty of the actual condition of the site, the more fully the dispatcher knows the actual condition of the site, the more accurate the estimated value of the time required for repairing the fault node or the line is, so that each fault element corresponds to a plurality of repairing times.
The difference between the scheme 1 and the scheme 2 in the main calculation example is whether the addition of the V2G influences the power supply recovery process of the system, and the difference between the scheme 2 and the scheme 3 in the sub calculation example is whether the addition of the rolling time domain optimization influences the post-disaster load loss of the urban power grid.
The specific implementation steps are as follows:
in the main calculation example, a scheme 1 and a scheme 2 are selected for comparison analysis, and whether the addition of V2G influences the power supply recovery process of the system is observed. In the scheme 2, after the disaster is taken for 20 hours, the V2G charging pile participates in the output condition of the power restoration of the system, and the power data are detailed in a bar chart of fig. 5. The repair time of both schemes is scheduled by taking the first estimated value, and the repair path and repair sequence of the repair team are shown in fig. 6 and table 5 respectively. Table 6 is a table of the influence of the V2G charging pile and EV access on the power system power restoration in both cases in this main calculation example, regardless of the rolling time domain optimization first, only from the point of view of the presence or absence of the V2G charging pile and EV access.
Table 5 repair team repair order schematic table for two cases
TABLE 6 schematic table of total cut load (kW.h) during maintenance
| Scheme 1 | |
| 4100 | 2583.5 |
As can be seen from Table 5, the two schemes are only different in order of repairing faulty wiring 15-16 and 10-13, corresponding to repair team 3 of scheme 1 and repair team 2 of scheme 2, respectively. In scheme 1, although repair of the line 15-16 takes more time than repair of the line 10-13, repair crews may prioritize restoration of power to the node 16 and surrounding nodes, and after the scheme 2V2G charging piles inject power to the nodes, the loads of the nodes 13, 14, 15, 16 are all power-guaranteed, at which time repair of the line 10-13 may be prioritized from a repair time perspective. The subsequent repair sequence is substantially identical in both cases, with another repair team cooperating to repair the generator outlet line while repairing the failed node 10 and node 3. While analyzing Table 6, the participation of V2G and EV greatly facilitates system power restoration, resulting in a 36.99% reduction in grid cut load during maintenance. Therefore, by combining the two cases, the V2G charging pile and the EV can be connected to relieve the load power loss condition of the node and the peripheral nodes, and reduce the power loss load; on the other hand, the load importance can be influenced to a certain extent, so that the repair route of a maintenance team is changed, nodes and peripheral circuits with large loss load quantity and short repair time under the support of the V2G are repaired preferentially, and the loss load quantity is reduced while the recovery process of power supply of the system is quickened.
In the sub-calculation example, a scheme 2 and a scheme 3 are selected for comparison analysis, and the influence of rolling time domain optimization on the post-disaster load loss of the urban power grid is analyzed. The results of main algorithm 2 are listed here together, with the repair team repair path and repair order in both cases shown in fig. 7 and table 7, respectively. Table 8 shows the total cut load during maintenance in both cases.
Table 7 repair team repair order schematic table for two cases
TABLE 8 schematic Table of total cut load (kW.h) during maintenance
| |
|
| 2583.5 | 2276 |
Both schemes of the sub-calculation example take the premise of V2G charging pile and EV access, except that scheme 3 utilizes a rolling time domain optimization method, and the corresponding fault element repair time is updated when rolling each time. Because the repair time of the line and the node is inevitably changed due to the change of the actual situation as the repair process is carried out, the analysis enables the repair team to schedule more real-time and accuracy.
In the repairing process, when the scheduling process is performed, repairing time of some faulty nodes or lines can be dynamically changed, so that the load recovery sequence is affected. For nodes or lines with low importance of the perimeter load and long repair time, repair teams typically choose to repair in a later order. Since node 10 is more heavily loaded than node 3 and the recovery time is relatively shorter, both schemes would prefer to repair node 10 and the surrounding faulty line. Restoring the load of node 3 requires repairing lines 2-30 or lines 25-37 at the same time as repairing node 3. And the longest maintenance time among the three is node 3, followed by faulty line 25-37. Because of the limitation of the resources carried by the maintenance team, both scheme 2 and scheme 3 have only maintenance team 1 the ability to repair node 3, while scheme 3 selects maintenance team 2 to repair line 2-30 because repair line 10-13 requires less time than repair line 13-14 and because line 2-30 has less repair time after dynamic update, thus restoring the load at node 3 2.5 hours ahead of scheme 2. The dynamic update of the maintenance time enables the dispatching to be more practical and real-time, accelerates the power supply recovery process and has better recovery effect. Also analysis of table 8 shows that scheme 3 uses a rolling optimization method to reduce the grid cut load by 11.90% during maintenance as compared to scheme 2.
In conclusion, the method can be effectively applied to the scheduling work of the post-disaster maintenance team of the urban power system, fully excavates the V2G potential, enhances the post-disaster quick recovery capability of the power system, reduces the economic loss of the city caused by power failure, and enhances the elasticity of the urban power system.
FIG. 8 is a schematic diagram of the system functional modules of the system of the present invention: the system for realizing the dynamic elastic power supply recovery method of the urban power system under typhoon disasters comprises a data collection module, a rush repair operation module, a scheduling module and an output module; the data collection module, the rush-repair operation module, the scheduling module and the output module are sequentially connected in series; the data collection module is used for carrying out an operation period on the V2G system during disaster early warning, collecting road network EV traffic data and uploading the data to the rush-repair operation module; the emergency repair operation module is used for temporarily providing electric energy support for the urban power grid by adopting EV resources at disaster according to the received data, dispatching a maintenance team to perform emergency repair operation on a fault line according to traffic data, and uploading the data to the scheduling module; the scheduling module is used for updating the first-aid repair scheme in real time by adopting an optimized scheduling method for considering V2G data by a maintenance team before repairing the next fault equipment according to the received data, repairing the next fault equipment according to the updated first-aid repair scheme, and transmitting the data out of the module and the first-aid repair operation module; the output module is used for repeating the rush repair operation module and the scheduling module until all the fault devices are completed in the rush repair process, and outputting the result.
Claims (6)
1. A dynamic elastic power supply recovery method of an urban power system under typhoon disasters comprises the following steps:
s1, during disaster early warning, a V2G system performs an operation period and collects road network EV traffic data;
s2, when a disaster comes, the V2G interaction station adopts EV resources to provide electric energy support for the urban power grid, and dispatches maintenance teams to carry out rush repair operation on the fault line according to traffic data;
s3, before repairing the next fault equipment, a maintenance team updates the first-aid repair scheme in real time by adopting an optimized scheduling method considering V2G data, and repairs the next fault equipment according to the updated first-aid repair scheme;
s4, repeating the steps S2 to S3 until all the fault devices are salvaged.
2. The method for recovering dynamic elastic power supply of urban electric power system under typhoon disaster according to claim 1, wherein the collecting road network EV traffic data in step S1 specifically comprises the following steps:
modeling a traffic network as G= (V, E, A), wherein G is a traffic network node connection line set, V is a traffic node set, E is a traffic connection set, A is a traffic network adjacency matrix and is used for describing the connection distance between each node; based on the traffic network adjacency matrix A, a shortest path running scheme between any two points in the traffic network is obtained through a shortest path algorithm;
Modeling of traffic network and power system coupling:
the coupling between the traffic network and the power system is equivalent to the connection between nodes in the network, and a 0-1 variable mathematical model is constructed to represent the coupling relation:
ξ＝{ε αβ ∈V α V β }
wherein xi is the collection of connecting lines between the traffic network and the power system; epsilon αβ The system comprises a coupling line set formed by a traffic network and a power system; v (V) α Is a traffic node set; v (V) β The method is a power grid node set;
for a 0-1 variable representing whether there is an interlayer node connection between the traffic network and the power system, if +.>
The coupling relation between the traffic network node i and the power grid node j is shown, if +.>
The traffic network node i and the power grid node j are not in coupling relation; />
Is the ith node of the traffic network; />
Is the j node of the power grid;
travel chain modeling based on travel willingness:
dividing an urban traffic network into three areas of a residential area, a commercial area and a scenic area, and dividing nodes of the three areas into sets H, B and S; the daily travel path of the user is regarded as a travel chain and passes through 2 area nodes and 3 area nodes:
Link＝{D 0 ,D f ,R 0f ,L 0f ,t 0 ,t f ,T 0f ,t p }
link is a travel chain state variable set; d (D) 0 Is the starting point of the travel chain; d (D) f The travel chain end point; r is R 0f Is a travel path; l (L) 0f Is the path length; t is t 0 The travel time is the travel time; t is t f Is the arrival time; t (T) 0f Is the driving duration; t is t p Is the parking time length;
introducing a travel willingness coefficient rho to correct the total travel willingness change of the EV user for simulating the influence of the environment on the travel willingness of the user:
n up ＝ρn total
in n up EV number for willing travel; n is n total The total EV number;
the driving time T of the mth journey is expressed by the following formula 0f,m Time of arrival t f,m And the departure time t of the next journey 0,m+1 ：
t f,m ＝t 0 +T 0f,m
t 0,m+1 ＝t f,m +t p
D in m Length of the mth journey;
the average speed of the EV driving on the mth journey;
the departure time t of entering road network for the first time every day first (τ) is
Departure time t of last entering road network final (tau) is->
Wherein sigma is the standard deviation; mu is the average trip time; τ is more than 0 and less than or equal to 24;
EV real-time state modeling as
Wherein W is a set of real-time state information of each EV and is SOC a For the state of charge set of all EVs at time a, ψ a For the driving status set->
A V2G interaction station number set closest to the position where the moment a is located;
modeling the real-time state of the interaction station:
in the early period of disaster, the space-time state of the V2G interaction station is expressed as
V＝{SOV a ,P a ,λ a }
Wherein V is a set of respective real-time state information of each V2G interaction station at the time a; SOV (solid oxide Fuel cell) a The operation state set of all the V2G interaction stations at the moment a is set; p (P) a Is a rated capacity set; lambda (lambda) a Is a set of region coefficients.
3. The method for recovering dynamic elastic power supply of urban power system under typhoon disaster as claimed in claim 2, wherein step S2 is characterized by dispatching maintenance crews according to traffic data to repair the faulty line, and specifically comprising the steps of:
corresponding action time sequences are respectively established for typhoons, dispatching centers, V2G interaction stations and maintenance teams, and time periods are divided into four key nodes: t is t 0 The method comprises the steps that the model starting time is set time before disaster early warning; t is t 1 The disaster early warning time is the disaster early warning time; t is t 2 The moment when typhoon disasters affect the beginning is reached, and then the urban power system faults begin; t is t 3 At the moment of fault repair, the urban power system is recovered to be normal;
typhoon disasters are divided into four stages: a pre-disaster period, a mid-disaster period, a post-disaster period and a disaster recovery period; the urban power system is divided into a conventional period, a fault period and a conventional period according to the coming moment of typhoon disasters; the dispatching center is divided into a dormancy period, a response period, a dispatching period and a re-coiling period according to the early warning time and the coming time of the typhoon disaster; the V2G system is divided into: output period-operation period-input period-output period; the maintenance team is divided into a sleep period, a standby period, a rush repair period and a sleep period;
And confirming the path states among fault devices through the urban road network traffic data provided by the V2G, and adjusting the rush-repair scheme in real time according to traffic conditions.
4. The method for recovering dynamic elastic power supply of an urban power system under typhoon disasters according to claim 3, wherein the method for optimizing and scheduling V2G data in step S3 updates a rush repair scheme in real time, specifically comprising the following steps:
updating the state of the maintenance team according to different time periods:
the maintenance team is in a ready state during the standby period: the V2G interaction station enters an operation period to collect urban road network information and peripheral EV data and coordinate regional power system recovery to the dispatching center; after the disaster occurs, the moment when the urban power system is in fault is taken as a critical point between a standby period and a rush-repair period;
the maintenance team enters a rush-repair period: immediately starting to perform fault recovery on the urban power system by adopting a rolling time domain optimization method;
the maintenance team adopts a rolling time domain optimization method to schedule in the rush-repair period: the maintenance team repairs the line ij first; the time when the ith device is reached is 0 time when the device is repaired, and the time for repairing the device comprises repair time and time for going to the next device;
After the maintenance team reaches the ith fault equipment, judging whether the carried repair materials can meet the repair conditions of the equipment or not: if yes, repairing is carried out; if the current line is insufficient, reporting to an upper dispatching center and directly going to the next line for rush repair.
5. The method for recovering dynamic elastic power supply of urban power system under typhoon disaster as defined in claim 4, wherein the rolling time domain optimizing method comprises the following steps:
the model aims at enabling the total load shedding amount of the power system to be minimum in a set time; the objective function is expressed as
Wherein h is the current moment; t is the number of time periods contained in the optimization time used; v is a node set; omega shape j The weight of the node load;
for load shedding power;
the set constraint conditions are as follows:
work angle constraint:
and->
In B of ij For line admittance, θ i,t For the phase angle of the line>
For line power, M is a set constant, u ij,t Is a variable of 0-1 and u ij,t =1 indicates that the line is in normal operation, u ij,t =0 indicates that the line is in an abnormal operation state, L is a transmission line set;
output constraint of the generator set:
wherein->
For the lower limit of the output power of the generator set, +.>
For the upper limit of the output power of the generator set, < > >
The output of the generator is output, and G is a generator set;
V2G force constraint:
wherein->
Lower limit of output power of V2G interaction station, < >>
For the upper limit of the output power of the V2G exchange station, < >>
The output of the interaction station is V2G, and V2G is a set of generator sets;
line capacity constraint:
wherein->
Maximum apparent power for line capacity; u (u) ij Is a variable of 0-1, u ij =1 indicates that the line is in a normal operation state at time t;
node power balancing constraints:
where pi (j) is the parent node set of node j and delta (j) is the child of node jNode set,/->
For load demand, +.>
Is the load shedding amount;
load shedding amount constraint:
dispatch constraints for rush repair team:
wherein x is i,j,c The method comprises the steps that 0-1 variable from the fault equipment i to j of the c emergency repair team is used, EC is a set of all emergency repair teams, N is a set of the fault equipment, S is a starting point set of the emergency repair team, and R is a final point set of the emergency repair team;
and (5) starting, finishing, entering and exiting constraint of the rush repair team:
the first constraint indicates that the rush-repair team must start from the starting point, the second constraint indicates that the rush-repair team must return to the end point, the third constraint indicates that the rush-repair team cannot go from the end point to the next fault device, and the fourth constraint indicates that the rush-repair team cannot go from the fault device to the starting point;
unrepeatable repair constraints: 
Wherein the constraint indicates that the rush repair team cannot leave a faulty device and reach the faulty device immediately;
whether the faulty device has been salvaged, using the variable y i,c Record, expressed as
Rush repair team leaving equipment record constraint:
wherein y is i,c For rush repair team leave record, y i,c A variable of 01, and if the fault equipment is recovered to be normal when the rush repair team leaves the fault equipment, the value is 1;
material carrying constraint:
wherein Bac i Materials required for repairing the faulty device i +.>
Carrying the total amount of materials for the rush repair team;
road network congestion constraints:
the method is characterized in that the road from the fault device i to the fault device j can not pass under the condition of traffic jam;
repair time constraint:
wherein->
Repair time length of fault device i for rush repair team c,/-for the fault device i>
For the length of the traffic of the rush repair team c from the faulty device i to the faulty device j, +.>
The moment from the fault device i to the fault device j is the first-aid repair team c; timing when rush repair team reaches fault equipment i from starting pointTime 0 is defined as>
Time constraint of repair equipment for repairing faults of rush repair team is +.>
Wherein f i,t A variable of 0-1 is used for recording whether the fault equipment i is repaired completely, if the fault equipment i is repaired completely within a discrete period t, the value is 1, otherwise, 0 is taken;
repair times constraint of rush repair team:
Each faulty device can be repaired at most once, expressed as
If the fault equipment i is not repaired, the moment when the rush repair team c reaches the equipment is 0, and the constraint expression is that
The device recovers the tidal current coupling constraint:
the fault equipment i is recovered to a normal state after repair is completed, the fault equipment state needs a variable record before and after repair, and the variable is expressed as:
wherein BB is a set of failed nodes; v i,t 01 variable which is the state of the equipment, and 1 if the equipment is repaired;
the line from the faulty device i to the device j can participate in operation after repair is completed, but the participation in tidal current operation after repair requires superior scheduling constraint expressed as
BL is a fault line set; u (u) ij,t The variable is 0-1 of whether the line participates in operation or not, and if the line participates in operation, the variable is 1;
if the faulty equipment is not repaired, the line with the faulty equipment still is regarded as faulty and cannot participate in operation, and if one of the two sides of the line ij is still in a faulty state, the line is also in the faulty state, and the expression is as follows:
in specific implementation, mixed integer linear programming modeling is carried out, and the solution is carried out through a computer solver.
6. A system for realizing the dynamic elastic power supply recovery method of the urban power system under typhoon disasters according to one of claims 1 to 5, which is characterized by comprising a data collection module, a rush repair operation module, a scheduling module and an output module; the data collection module, the rush-repair operation module, the scheduling module and the output module are sequentially connected in series; the data collection module is used for carrying out an operation period on the V2G system during disaster early warning, collecting road network EV traffic data and uploading the data to the rush-repair operation module; the emergency repair operation module is used for temporarily providing electric energy support for the urban power grid by adopting EV resources at disaster according to the received data, dispatching a maintenance team to perform emergency repair operation on a fault line according to traffic data, and uploading the data to the scheduling module; the scheduling module is used for updating the first-aid repair scheme in real time by adopting an optimized scheduling method for considering V2G data by a maintenance team before repairing the next fault equipment according to the received data, repairing the next fault equipment according to the updated first-aid repair scheme, and transmitting the data out of the module and the first-aid repair operation module; the output module is used for repeating the rush repair operation module and the scheduling module until all the fault devices are completed in the rush repair process, and outputting the result.
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