CN112837172A - Power distribution network post-disaster first-aid repair decision method considering information fusion of traffic network and power distribution network - Google Patents

Power distribution network post-disaster first-aid repair decision method considering information fusion of traffic network and power distribution network Download PDF

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CN112837172A
CN112837172A CN202011559609.4A CN202011559609A CN112837172A CN 112837172 A CN112837172 A CN 112837172A CN 202011559609 A CN202011559609 A CN 202011559609A CN 112837172 A CN112837172 A CN 112837172A
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power distribution
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CN112837172B (en
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谢云云
李尚轩
吴昊
蔡胜
苏晓茜
严欣腾
李虹仪
杨皖浙
罗瑞丰
时涵
王振刚
胡红新
付豪
邹云
殷明慧
卜京
张俊芳
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Nanjing University of Science and Technology
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Abstract

The invention discloses a power distribution network post-disaster rush-repair decision method considering information fusion of a traffic network and a power distribution network, which is used for establishing a rush-repair strategy optimization model considering traffic network interruption according to acquired non-electric quantity information such as road congestion interruption under typhoon disasters in order to adapt to the change of an external road environment; and finally, coordinating resources such as construction teams, rush-repair materials, emergency mobile power supplies, power distribution network racks, distributed power supplies and the like on the premise of meeting the safe and stable operation constraint of the distribution network, and deciding the material transportation, construction team restoration, mobile power supply scheduling sequence, network rack reconstruction and island division schemes by aiming at minimizing load loss. The invention fully considers the cooperative optimization of fault first-aid repair and load recovery, achieves the aim of minimum economic loss and simplifies the coding of each part of solution.

Description

Power distribution network post-disaster first-aid repair decision method considering information fusion of traffic network and power distribution network
Technical Field
The invention belongs to the field of emergency repair scheduling of power systems, and relates to a decision method for emergency repair of a power distribution network after a disaster, which takes information fusion of a traffic network and the power distribution network into consideration.
Background
Due to the fact that the design standard of the power distribution network is low, a large number of physical faults of the power distribution network can be caused in the process of natural disasters (such as storm, typhoon and the like). After a disaster occurs, construction teams need to arrange the rush-repair sequence according to the post-disaster fault condition. The reasonable and efficient fault first-aid repair sequence can reduce the power failure time and improve the reliability of the power distribution network. For this reason, it is necessary to provide support for emergency repair strategies after power distribution network disasters.
In the current research of the post-disaster emergency repair strategy of the power distribution network, the traditional method only considers load recovery means such as personnel material scheduling or switching sequence. However, in the actual post-disaster repair process of the power distribution network, the problem of vehicle path caused by the optimized personnel material scheduling can be influenced by traffic conditions. Natural disasters can not only cause distribution network faults, but also cause damage to traffic roads. In the post-disaster recovery process, the travel path and time of personnel and materials are important influence factors during the emergency repair strategy making, the real-time and accurate traffic information can optimize the power grid emergency repair strategy, the influence of traffic road conditions on emergency repair decisions is reduced to the maximum extent, and the recovery after the disaster is accelerated.
Disclosure of Invention
The invention aims to solve the problems that the current power distribution network post-disaster emergency repair only considers personnel material scheduling or load recovery means, the influence of congestion and interruption caused by traffic network damage under natural disasters on emergency repair decision is not introduced, and the goal of minimum economic loss cannot be achieved, and provides a power distribution network post-disaster emergency repair decision method considering information fusion of a traffic network and a power distribution network.
The technical solution for realizing the purpose of the invention is as follows: a power distribution network post-disaster repair decision-making method considering information fusion of a traffic network and a power distribution network comprises the following steps:
step 1, acquiring non-electric quantity information of road congestion or interruption under a typhoon disaster, and establishing an information fusion framework of a traffic network and a power distribution network, wherein the power distribution network maps disaster damage position information, material demand information and geographical coupling information of an MPS access point to the traffic network, and the traffic network feeds back fault point repair state information, warehouse material storage information and MPS available state information to the power distribution network;
step 2, calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
step 3, clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage amount is close to the distance between the warehouses and the fault points;
step 4, under the precondition that the scheduling constraint, the material distribution constraint, the fault repair time constraint, the mobile emergency power generation vehicle constraint and the safety and stability operation constraint of a power distribution network emergency repair decision model are met, establishing a power distribution network emergency repair decision model based on grid reconstruction and island division;
and 5, solving the distribution network emergency repair decision model by adopting an upper-layer and lower-layer iterative solving method, solving the upper-layer model, the genetic algorithm and the Prim algorithm by adopting a simulated annealing algorithm to solve the lower-layer model, and solving to obtain an optimal distribution network emergency repair decision scheme.
Compared with the prior art, the invention has the advantages that:
(1) the invention fully considers the congestion and interruption caused by the damage of the traffic network under natural disasters, and the cooperative optimization of fault first-aid repair and load recovery, achieves the aim of minimum economic loss, simplifies the coding of each part of solution, and finally obtains the detailed allocation scheme of load recovery and fault first-aid repair.
(2) Compared with the traditional post-disaster power distribution network emergency repair strategy, the method fully considers the problems of traffic network congestion, interruption and the like under natural disasters, refines the coupling relation of key nodes of the traffic network and the power distribution network on the geographical position, simplifies the number of nodes in traffic topology through a Floyd shortest path algorithm, and reduces the scale of the subsequent optimization problem by adopting a clustering algorithm.
(3) The invention considers the mutual influence of the Vehicle Routing Problem (VRP) and the load recovery scheduling, so that the personnel material scheduling and the power grid scheduling efficiently cooperate, a detailed emergency repair scheme can be provided for an electric power company, the power supply of a user is ensured to be rapidly recovered, and the economic loss is reduced.
(4) The invention finally obtains the detailed allocation scheme of the connecting lines, the distributed power supplies, the personnel materials and the MPS by adopting the upper and lower layer iterative solution method, thereby not only improving the overall precision to achieve the global optimal solution, but also greatly reducing the iterative time compared with a single intelligent calculation method.
The present invention will be described in detail with reference to the accompanying drawings.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flowchart of the upper and lower layer iteration solution of the present invention.
Fig. 3 is a PG & E69 node standard system diagram adopted in the embodiment of the present invention.
FIG. 4 is a graph comparing load recovery curves for examples of the present invention.
FIG. 5 is a graph of algorithm convergence in an embodiment of the present invention.
Detailed Description
With reference to fig. 1, a power distribution network post-disaster first-aid repair decision method considering information fusion of a traffic network and a power distribution network includes the following steps:
step 1, acquiring non-electric quantity information of road congestion or interruption under a typhoon disaster, and establishing an information fusion framework of a traffic network and a power distribution network, wherein the power distribution network maps disaster damage position information, material demand information and geographical coupling information of an MPS access point to the traffic network, and the traffic network feeds back fault point repair state information, warehouse material storage information and MPS available state information to the power distribution network;
the topological structure of the traffic network and the road traffic condition directly influence the spatial position of the vehicle, the spatial position determines the material satisfying time, the fault point repairing time and the generator car access time, and the three times simultaneously influence the topological state of the current power distribution network. And the cross effect of the two-network coupling information further determines the optimization effect of the post-disaster emergency repair decision quantity of the power distribution network.
Constructing a road traffic network by using the topological structure relationship of each road, and supporting a road network system by constructing a node set, a road grade set, a road section connection relationship, a disaster situation and length set, intersection delay and a road traffic capacity set, so that the road network system can reflect the network scale, the road grade, the road section length, the highest speed limit and the post-disaster traffic capacity road attribute, and can meet the requirement of subsequent shortest path analysis;
the distribution network provides physical support for planning of rush-repair teams, goods and materials and specific emergency power supply paths for mapping disaster position information, goods and materials demand information and MPS access point geographical coupling information of the traffic network, and meanwhile, the traffic network feeds back fault point repair state information, warehouse reserve information and MPS available state information to the distribution network so as to determine a scheduling scheme during rush-repair and calculate load recovery.
Step 2, considering the huge scale of the actual traffic network, calculating the shortest path between the key nodes of the traffic gateway by using a Floyd algorithm, reducing the topological scale to only contain the key nodes, and specifically expanding the following steps:
step 2-1: determining the equivalent distance between two adjacent points in the topology:
Figure BDA0002859000480000031
in the formula (d)xyIs the distance between x and y, λxyThe vehicle speed correction coefficient is set according to the road type, and reflects the damage degree of the vehicle speed correction coefficient affected by the typhoon disaster; alpha is alphaxyIs a variable, is 1 communicated from x to y, otherwise is 0, M is a maximum value, DxIndicating the delay time of intersection x, DyIndicating the delay time at intersection y;
step 2-2: and calculating the shortest path between any two fault points in the topology and the warehouse by using a Floyd algorithm, and if the two fault points are not connected indirectly, judging that the two fault points are disconnected.
After the key nodes of the power distribution network are mapped to the traffic network, fault points and MPS access points in the traffic network are regarded as two-network coupling nodes, the coupling nodes and the warehouse are both regarded as key nodes in the power distribution network and are mapped to the traffic network, and a plurality of road nodes exist between the coupling nodes and the warehouse, so that the influence of road types and road conditions on arrival time is reflected.
Step 3, in order to further increase the calculation speed, dividing the fault points into a plurality of clusters before path planning according to the number of the warehouses, and clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage amount is close to the distance between the warehouses and the fault points, so that the clustering process is only performed on the traffic network, and specifically comprises the following steps:
matching the fault points with the warehouses by adopting spectral clustering, dividing the fault points into a plurality of clusters before path planning according to the number of the warehouses, wherein a clustering model is as follows:
Figure BDA0002859000480000041
Figure BDA0002859000480000042
Figure BDA0002859000480000043
Figure BDA0002859000480000044
in the formula, d (dep)σM) represents the distance of the warehouse dep belonging to the cluster σ from the failure point m; sσ,mThe variable is 0-1, the fault point m is allocated to the cluster sigma to be 1, otherwise, the cluster sigma is 0;
Figure BDA0002859000480000045
representing the stock storage of the warehouse within the cluster sigma,
Figure BDA0002859000480000046
representing the amount of material required by the fault point m;
the target function represents that the fault point is distributed to the nearest warehouse according to the shortest equivalent distance, the constraint condition represents that one fault point is only distributed to one warehouse, and the warehouse capacity of the cluster sigma is not less than the material demand of the fault point in the cluster;
after clustering, the same cluster only contains one warehouse and a plurality of fault points, and the fault points can only be accessed by construction teams and material vehicles in the same cluster.
Step 4, under the precondition that the scheduling constraint, the material distribution constraint, the fault repair time constraint, the mobile emergency power generation vehicle constraint and the safe and stable operation constraint of the power distribution network are met, the power distribution network emergency repair decision model based on the net rack reconstruction and the island division is established,
the power distribution network emergency repair decision model specifically comprises the following steps:
the method comprises the following steps of determining a decision model objective function by taking the objective of reducing the load loss caused by the fault of the power distribution network as much as possible:
Figure BDA0002859000480000047
in the formula (I), the compound is shown in the specification,
Figure BDA0002859000480000048
load weight of a node i of the power distribution network; rhoi,tIf the power distribution network node i is communicated at the moment t, the communication is 1, otherwise, the communication is 0;
Figure BDA0002859000480000049
and carrying active load for the power distribution network node i at the moment t.
The construction team scheduling constraint, material distribution constraint, fault repair time constraint, mobile emergency power generation vehicle constraint and power distribution network safety and stability operation constraint of the power distribution network emergency repair decision model are specifically as follows:
(1) construction team scheduling constraint:
the construction team arriving at the failure point also leaves from that point after the repair is completed:
Figure BDA0002859000480000051
Figure BDA0002859000480000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002859000480000053
representing that the construction team c in the cluster sigma moves from m to n and is 1, otherwise, the construction team c is 0;
Figure BDA0002859000480000054
representing that the construction team c in the cluster sigma moves from n to m and is 1, otherwise, the construction team c is 0; RC (resistor-capacitor) capacitorσRepresenting a construction team set; dp is warehouse node, NσA failure point and a warehouse point set are obtained;
the construction team starts from the warehouse:
Figure BDA0002859000480000055
wherein the content of the first and second substances,
Figure BDA0002859000480000056
representing that the construction team c in the cluster sigma moves from dp to m;
the construction team finally returns to the warehouse:
Figure BDA0002859000480000057
in the formula (I), the compound is shown in the specification,
Figure BDA0002859000480000058
representing that the construction team c in the cluster sigma moves from m to dp, which is 1, otherwise, is 0; ncσThe number of construction teams in the cluster sigma;
any point of failure can only be accessed by one team:
Figure BDA0002859000480000059
in the formula (I), the compound is shown in the specification,
Figure BDA00028590004800000510
whether the construction team c visits the fault point m is 1, and if not, the fault point m is 0;
a construction team c arrives at n from m, then c must visit m:
Figure BDA00028590004800000511
(2) and (3) material vehicle behavior constraint:
the total amount of materials allocated to the transport vehicle is not more than the amount of available materials of the warehouse in the cluster:
Figure BDA00028590004800000512
in the formula (I), the compound is shown in the specification,
Figure BDA00028590004800000513
is the actual carrying capacity of the vehicle v;
Figure BDA00028590004800000514
(ii) a cluster sigma-inner bin capacity;
upper limit of material capacity of the transport vehicle v:
Figure BDA0002859000480000061
in the formula, CapvIs the maximum carrying capacity of the vehicle v;
the transport vehicle arriving at the failure point leaves the point after delivering the material and moves to and from the point:
Figure BDA0002859000480000062
Figure BDA0002859000480000063
in the formula (I), the compound is shown in the specification,
Figure BDA0002859000480000064
indicating that the vehicle v is moving from m to dp is 1, otherwise it is 0;
Figure BDA0002859000480000065
indicating that vehicle v is moving from dp to m, is 1, otherwise is 0; VEσA set of transport vehicles within cluster σ;
the transport vehicle starts from the warehouse and finally returns to the warehouse:
Figure BDA0002859000480000066
Figure BDA0002859000480000067
in the formula, nvσNumber of vehicles within cluster σ;
the minimum number of vehicles meeting the material requirement of a fault point m is as follows:
Figure BDA0002859000480000068
Figure BDA0002859000480000069
in the formula (I), the compound is shown in the specification,
Figure BDA00028590004800000610
the quantity of materials required for the fault point m;
Figure BDA00028590004800000611
whether the transport vehicle v reaches the fault point m is represented as 1, and otherwise, the transport vehicle v is represented as 0;
if a vehicle v travels from m to dp, v must visit m:
Figure BDA00028590004800000612
if the vehicle v travels from m to the next destination point n, v must visit m:
Figure BDA00028590004800000613
Figure BDA00028590004800000614
wherein the content of the first and second substances,
Figure BDA00028590004800000615
represents that the transport vehicle v moves from the fault point m to n, is 1, otherwise is 0;
time constraint for the vehicle to reach the next fault point n:
Figure BDA0002859000480000071
Figure BDA0002859000480000072
Figure BDA0002859000480000073
Figure BDA0002859000480000074
wherein, TPn,vThe time at which the vehicle v reaches the fault point n; TPm,vFor the time, tr, at which the vehicle v reaches the fault point mm,dp,vThe distance time, tr, for the fault m and the warehouse dpdp,n,vThe distance between the warehouse dp and the fault point n;
if the vehicle v reaches the failure point m once, the time of reaching the point is set to 0:
Figure BDA0002859000480000075
Figure BDA0002859000480000076
the material meeting time of the fault point m is the time of a transport vehicle arriving at the point at the latest:
Figure BDA0002859000480000077
Figure BDA0002859000480000078
(3) time constraint for fault repair:
construction team arrival time constraint:
Figure BDA0002859000480000079
Figure BDA00028590004800000710
wherein, ATm,c、TPm,vRepresenting the time of the construction team c and the transport vehicle v reaching the fault point m; ATn,cRepresenting the time when the construction team c reaches the fault point n; r ism,cTime required for maintenance of the fault point m for the construction team c; trm,n,cThe time required for construction team c to move from m to n.
Vehicle arrival time constraints:
Figure BDA00028590004800000711
Figure BDA00028590004800000712
wherein, trm,dp,vRepresents the time required for the transport vehicle v to move from the trouble point m to the warehouse dp;
any failure point is repaired only once:
Figure BDA00028590004800000713
wherein f ism,tWhether the fault point m is repaired at the moment t is represented as 1, and if not, the fault point m is 0;
maintenance starting time AP of construction teamm：
Figure BDA0002859000480000081
Figure BDA0002859000480000082
After the construction of the construction team is completed, the state of the fault point is set as 1:
Figure BDA0002859000480000083
Figure BDA0002859000480000084
Figure BDA0002859000480000085
Figure BDA0002859000480000086
if the construction team does not visit the fault point m, the APmSetting 0:
Figure BDA0002859000480000087
Figure BDA0002859000480000088
the repaired failure point will be available in the following time:
Figure BDA0002859000480000089
Figure BDA00028590004800000810
wherein z ism,tIndicating whether the fault point m is available at the moment t, if so, the fault point m is 1, otherwise, the fault point m is 0;
when the point of failure state is not repaired, force
Figure BDA00028590004800000811
Coupling construction team to distribution network constraints for 0:
Figure BDA00028590004800000812
Figure BDA00028590004800000813
wherein the content of the first and second substances,
Figure BDA00028590004800000814
the line L which represents the fault point m is available at the time t, is 1, otherwise is 0;
(4) restraint of the mobile emergency power generation vehicle:
MPS arriving at the access point also leaves this point after the power supply is over:
Figure BDA00028590004800000815
Figure BDA00028590004800000816
wherein the content of the first and second substances,
Figure BDA00028590004800000817
the fact that the power generation car s moves from the access point e to the access point f is 1, and otherwise, the number is 0;
Figure BDA00028590004800000818
indicating that the generator car s moves from access point f to e, is 1, otherwise is 0, MσIs a set of access points;
MPS goes from warehouse and finally back to warehouse:
Figure BDA0002859000480000091
Figure BDA0002859000480000092
wherein the content of the first and second substances,
Figure BDA0002859000480000093
indicating that the generator car s is moved from the warehouse dp to e, is 1, otherwise is 0;
Figure BDA0002859000480000094
indicating that the generator car s is moved from e to the warehouse dp, is 1, otherwise 0, nsσNumber of MPS in a cluster, MPSσThe method comprises the steps of (1) collecting a generator car;
any MPS access point can only be accessed by one MPS:
Figure BDA0002859000480000095
Figure BDA0002859000480000096
wherein
Figure BDA0002859000480000097
The method comprises the steps that whether a power generation vehicle s accesses an access point e or not is indicated, wherein different from a construction team, a fault point must be accessed by a construction team, and all access points do not need to be accessed;
if the generator car s arrives at f from e, s must visit e.
Figure BDA0002859000480000098
Figure BDA0002859000480000099
Time of MPS arrival at next access point f:
Figure BDA00028590004800000910
Figure BDA00028590004800000911
Figure BDA00028590004800000912
Figure BDA00028590004800000913
in the formula, MPf,sFor the time of arrival of the Generation Car s at the Access Point f, MPe,sFor the time the generator car s arrives at access point e,tre,f,stime, t, required for a generator car s to move from access point e to access point fe,sAnd (4) continuously supplying power for the power generation cars s at the access point e.
MPS arrival, departure time constraint:
Figure BDA00028590004800000914
Figure BDA00028590004800000915
Figure BDA00028590004800000916
Figure BDA00028590004800000917
wherein the content of the first and second substances,
Figure BDA00028590004800000918
respectively indicating that the power generation car s arrives at and departs from the access point e at the time t, and if the access point e is 1, otherwise, the access point e is 0;
the availability status of the access point for MPS power duration is 1:
Figure BDA0002859000480000101
Figure BDA0002859000480000102
Figure BDA0002859000480000103
wherein the content of the first and second substances,
Figure BDA0002859000480000104
whether the state of the access point e is available when the generator car arrives and leaves is 1 or 0; h ise,t,sIf the state in the power supply duration is available, the state is 1, otherwise, the state is 0;
(5) and (3) power distribution network safe and stable operation constraint:
MPS output constraint:
Figure BDA0002859000480000105
Figure BDA0002859000480000106
in the formula (I), the compound is shown in the specification,
Figure BDA0002859000480000107
the MPS is positioned at the active and reactive power output of a power distribution network node i at the moment t;
Figure BDA0002859000480000108
the maximum active and reactive output is obtained; h ism,t,sThe MPS is represented as 1 if the MPS is available at the power distribution network node i at the moment t, otherwise, the MPS is 0, and the MPS access points are part of nodes selected from the power distribution network nodes;
DG distributed power supply output constraint:
Figure BDA0002859000480000109
Figure BDA00028590004800001010
in the formula (I), the compound is shown in the specification,
Figure BDA00028590004800001011
active and reactive power output of a DG at a node i of the power distribution network at the moment t;
Figure BDA00028590004800001012
the maximum active and reactive output is obtained;
line power constraint:
Figure BDA00028590004800001013
Figure BDA00028590004800001014
wherein, Pk,t、Qk,tThe active power flow and the reactive power flow of the line k at the moment t are obtained;
Figure BDA00028590004800001015
the upper limit of active power and the upper limit of reactive power of the line k;
Figure BDA00028590004800001016
indicating whether the line k is available at the time t, and is 1, otherwise, is 0;
if line k is not switched or has a fault, its state is available:
Figure BDA0002859000480000111
wherein SW is a line set with a switch, NL is a damaged line set;
and (3) power distribution network tree-shaped operation constraint:
Figure BDA0002859000480000112
Figure BDA0002859000480000113
Figure BDA0002859000480000114
Figure BDA0002859000480000115
Figure BDA0002859000480000116
wherein, BS is a power supply set; beta is ai,j,tWhether the power distribution network node i is a father node of the power distribution network node j at the moment t is represented as 1, and if not, the number is 0; beta is aj,i,tWhether the power distribution network node j is a father node of the power distribution network node i at the moment t is represented as 1, and if not, the number is 0; nbtThe number of nodes of the power distribution network is;
branch flow constraint:
Figure BDA0002859000480000117
Figure BDA0002859000480000118
Figure BDA0002859000480000119
Figure BDA00028590004800001110
wherein the content of the first and second substances,
Figure BDA00028590004800001111
the active and reactive requirements of the node i of the power distribution network are met; rhoi,tThe communication state of the loads carried by the nodes i of the power distribution network is 1, otherwise, the communication state is 0; k (, i) represents the current flowing into the distribution network node i branch set, and K (i,) represents the current flowing out of the distribution network node i branch set;
node voltage constraint:
Figure BDA00028590004800001112
Figure BDA00028590004800001113
wherein, Vi,tThe voltage of a node i of the power distribution network at the moment t is obtained; rk、XkIs the impedance of line k; v1Is a reference voltage, VNIs a reference voltage;
and voltage deviation constraint:
Figure BDA0002859000480000121
wherein epsilon takes 10%;
once the load i recovers at time t, it will be forced to remain connected for a later time:
Figure BDA0002859000480000122
and 5, with reference to fig. 2, solving the distribution network emergency repair decision model by adopting an upper-layer iteration solving method and a lower-layer iteration solving method, solving the upper-layer model, the genetic algorithm and the Prim algorithm by adopting a simulated annealing algorithm, and solving to obtain an optimal distribution network emergency repair decision scheme, which specifically comprises the following steps:
the iterative solution algorithm specifically comprises the following steps:
step 5-1, solving a tie line action scheme by adopting a genetic algorithm; inputting parameters, wherein the variation rate is 0.2, the cross rate is 0.7, randomly generating an initial population pop matrix consisting of 0 and 1, the size of the matrix is a multiplied by c, a is 100, namely, the matrix represents that 100 chromosomes are contained in one population, the chromosomes are arrays with the length of c, and c represents the number of interconnections in the power distribution network, such as [ 010011 ], the current power distribution network contains 6 interconnections, the number of the corresponding position in the chromosome is the number of the interconnections, wherein 0 represents open, and 1 represents closed;
each chromosome represents a reconstruction scheme of a current rush-repair stage of a connecting line, all solutions in the pop need to meet radial operation constraint through depth-first search and verification, and meanwhile, branch flow constraint needs to be met, and if the solutions do not meet the branch flow constraint, a population needs to be regenerated;
step 5-2, solving a distributed power supply island division scheme by adopting a Prim algorithm; the distribution network topology contains 2 types of edges:
(3) the distribution network nodes are connected with the distribution network nodes to form edges;
(4) the distribution network nodes and the DG distributed power nodes are connected to form edges;
in the island searching process, edges formed by connecting power distribution network nodes and DG distributed power supply nodes are set to be 0, the priority is highest, the edges can be searched first, the load-carrying power distribution network nodes can be determined through the load power and the load grade connected to the edges, the weight of the edges is determined to be actually multi-index quantitative evaluation, the load power and the load weight comprise 2 indexes, the weight can be obtained through a formula, and the edge weight is calculated by the formula:
Wd＝λ1PNiλ2Sz
Figure BDA0002859000480000131
wherein, WdThe weight value of the d-th edge is; lambda [ alpha ]1、λ2The weight of the two indexes is the proportion of the two indexes in the weight; pmax、PminThe method comprises the following steps of (1) determining the maximum value and the minimum value of active load in a power distribution network needing to be subjected to islanding; pi、PNiThe active load value and the standard value of the ith power distribution network node are obtained; szThe load grades are divided into 1, 2 and 3 classes, and the values are respectively 100, 10 and 1;
5-3, solving the upper layer by adopting a simulated annealing algorithm; the cellular array coding is divided into two parts, namely a construction team path and a material vehicle path; such as
Figure BDA0002859000480000132
The first matrix represents the first-aid repair scheme of a construction team, A and B represent warehouses, 0 or 1 represents whether a failure point is reached or not, and the position of the warehouse represents the number of the failure point; taking the first row of the matrix as an example, representing that the construction team No. 1 is sent from the warehouse A, and the construction team goes to the fault points with the numbers of 1, 3 and 5 in sequence to carry out emergency repair work; the second matrix represents a dispatching scheme of the material vehicle, taking the first row as an example, the first row represents that the material vehicle No. 1 starts from the warehouse A, sequentially goes to the 1, 2, 3 and 5 fault points to supply materials, and returns to a warehouse to supplement materials each time when reaching one fault point;
step 5-4, after the repair time sequence of a certain fault point is obtained in step 5-3, and any fault point represents the change of a repaired or unrepaired repair state, calculating a grid reconfiguration scheme according to the current power distribution network topology by the genetic algorithm in step 5-1 to obtain a grid structure required by lower island division;
5-5, after the reconstruction scheme obtained in the step 5-4 is implemented, searching DG nodes existing in the power distribution network on the basis of the current network frame topology, solving an optimal island division strategy under the reconstruction scheme by adopting the Prim algorithm in the step 5-2, and calculating the load recovery amount of a lower layer;
and 5-6, disturbing the personnel material scheduling scheme on the upper layer according to the optimization rule of the simulated annealing algorithm in the step 5-3, wherein the disturbance method comprises two-point exchange, two-section exchange and point-section exchange, the disturbance method still is a feasible solution meeting the path planning constraint condition after disturbance, then repeating the steps 5-4 and 5-5 to calculate an objective function value, and finally obtaining the optimal power distribution network emergency maintenance decision scheme through outer layer iteration.
A power distribution network post-disaster rush repair decision system considering information fusion of a traffic network and a power distribution network comprises the following modules:
the two-network information fusion module: the system comprises a traffic network and power distribution network information fusion framework, a power distribution network information fusion framework and a power distribution network information fusion framework, wherein the traffic network and the power distribution network information fusion framework are established by the non-electric quantity information which is used for collecting road congestion or interruption under typhoon disasters;
and (3) shortest path calculation: calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
rush-repair target clustering module: clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage quantity meets and is close to the distance between the fault points;
the power distribution network emergency repair decision model building module comprises: the system is used for establishing a power distribution network emergency repair decision model based on network frame reconstruction and island division, and meeting construction team scheduling constraint, material distribution constraint, fault repair time constraint, mobile emergency power generation vehicle constraint and power distribution network safe and stable operation constraint conditions of the power distribution network emergency repair decision model;
a model solving module: and solving the power distribution network emergency repair decision model by adopting an upper-layer iteration solving method and a lower-layer iteration solving method, and solving an optimal power distribution network emergency repair decision scheme.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step 1, acquiring non-electric quantity information of road congestion or interruption under a typhoon disaster, and establishing an information fusion framework of a traffic network and a power distribution network, wherein the power distribution network maps disaster damage position information, material demand information and geographical coupling information of an MPS access point to the traffic network, and the traffic network feeds back fault point repair state information, warehouse material storage information and MPS available state information to the power distribution network;
step 2, calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
step 3, clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage amount is close to the distance between the warehouses and the fault points;
step 4, under the precondition that the scheduling constraint, the material distribution constraint, the fault repair time constraint, the mobile emergency power generation vehicle constraint and the safety and stability operation constraint of a power distribution network emergency repair decision model are met, establishing a power distribution network emergency repair decision model based on grid reconstruction and island division;
and 5, solving the distribution network emergency repair decision model by adopting an upper-layer and lower-layer iterative solving method, solving the upper-layer model, the genetic algorithm and the Prim algorithm by adopting a simulated annealing algorithm to solve the lower-layer model, and solving to obtain an optimal distribution network emergency repair decision scheme.
A computer-storable medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1, acquiring non-electric quantity information of road congestion or interruption under a typhoon disaster, and establishing an information fusion framework of a traffic network and a power distribution network, wherein the power distribution network maps disaster damage position information, material demand information and geographical coupling information of an MPS access point to the traffic network, and the traffic network feeds back fault point repair state information, warehouse material storage information and MPS available state information to the power distribution network;
step 2, calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
step 3, clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage amount is close to the distance between the warehouses and the fault points;
step 4, under the precondition that the scheduling constraint, the material distribution constraint, the fault repair time constraint, the mobile emergency power generation vehicle constraint and the safety and stability operation constraint of a power distribution network emergency repair decision model are met, establishing a power distribution network emergency repair decision model based on grid reconstruction and island division;
and 5, solving the distribution network emergency repair decision model by adopting an upper-layer and lower-layer iterative solving method, solving the upper-layer model, the genetic algorithm and the Prim algorithm by adopting a simulated annealing algorithm to solve the lower-layer model, and solving to obtain an optimal distribution network emergency repair decision scheme.
The invention is further described with reference to specific examples.
Examples
The PG & E69 node system is adopted in the embodiment to verify the effectiveness of the method, and the topological structure is shown in fig. 3;
red X represents a fault line, and is respectively F1(30-31), F2(33-34), F3(65-66), F4(88-89), F5(15-16), F6(20-21), F7(24-25), F8(35-36), F9(55-56) and F10(49-50), a blue dotted line represents a tie line, and is respectively Y1(10-70), Y2(12-20), Y3(14-90), Y4(26-54) and Y5(38-48), two warehouses are respectively D1 and D2 (not shown in the figure), and nodes 1-90 are load points.
The construction team has A, B, C, D four, and the goods and materials transport vechicle has six V1, V2, V3, V4, V5, V6, and the vehicle goods and materials ability of carrying is the same, all is 50.
Assuming that the faults occur simultaneously, the fault point disconnector opens immediately upon occurrence to isolate the fault.
The required rush repair time for each failure point is shown in table 1:
TABLE 1 node repair time
1 2 3 4 5 6 7 8 9 10
A 43 36 38 58 49 54 34 45 50 43
B 36 31 31 46 37 44 55 42 48 48
C 42 36 52 54 44 49 38 34 44 34
D 51 39 38 51 46 59 41 38 55 33
The load ratings are shown in table 2:
TABLE 2 load rating
Figure BDA0002859000480000151
Figure BDA0002859000480000161
The detailed solving process is as follows:
(1) at the beginning of a fault, an initial feasible solution is generated by a main program of a simulated annealing algorithm (namely, the solution meets logic constraints in a model), and the solution comprises a rush-repair team (the encoding mode of the solution is shown as follows: A, F2, F2, F8, F3, C, F1, F4, D, F10, F9, F5, B, F6 and F7), a material distribution vehicle (shown as:
Figure BDA0002859000480000162
) And a mobile emergency generator car (shaped as:
Figure BDA0002859000480000163
) Detailed task allocation of (1);
(2) maintenance by initial feasible solution and given journey timeTime, etc. according to the model, the repair time of all fault points can be obtained and arranged in sequence, like
Figure BDA0002859000480000164
The first column is a fault point number, and the second column is repair time;
(3) and sequentially calculating the target functions according to the obtained fault point repairing sequence. When a fault starts, all tie line switches and fault points are in a default state of 0 (disconnected), the topological structure of the power distribution network at the moment is converted into an adjacent matrix A (directed graph), the adjacent matrix A is read into a genetic algorithm, the genetic algorithm iterates according to a target function to obtain an optimal opening and closing scheme of the tie lines at the moment, the tie lines are supposed to be '11101', namely 'Y1', Y2 ', Y3 and Y5 are closed, Y4 is disconnected', at the moment, the tie lines immediately act according to '11101', the fault points are all disconnected, the current topology is searched according to depth priority to obtain nodes with points and power loss nodes, and the power loss amount from the '0 moment' to the period before the first fault point is repaired is calculated according to a model (stage I);
(4) circularly executing the step (3) to obtain total power loss of the initial feasible solution and the switching scheme of the connecting lines, such as
Figure BDA0002859000480000165
Taking the solution as the current optimal solution;
(5) disturbing the solution obtained in the step (1), calculating an objective function value of a new solution, if the objective function value is smaller than the current optimal solution, accepting the solution as the new optimal solution, otherwise, determining whether to accept the solution according to the Boltzmann probability;
(6) and (3) an iterative process of a simulated annealing main program: and iterating for n times at each temperature, reducing the temperature after n times according to a preset coefficient, and stopping circulation to obtain an optimal solution when the temperature is reduced to be lower than the termination temperature.
The solution in the iterative process has no practical significance, the convergence process is already embodied in the annealing curve, and only the obtained final solution is pasted here.
The simulation results are shown in table 3.
TABLE 3 Material satisfaction time at failure Point
Failure point Time of material satisfaction
4 61
1 157.7
2 234.1
3 275.2
8 307.3
6 479.4
5 517.6
7 577.6
10 519.8
9 686.9
Table 4 contains the construction team and generator car scheduling scheme, reconstruction scheme, and the point of failure repair time. Wherein Trans (1, F6) indicates that the construction team 1 departs to the fault point F6 at the time, C (Y1) indicates that the tie line Y1 is closed at the time, O (Y2) indicates open, recovered (4, F2) indicates that the fault point F2 is Repaired by the construction team 4 at the time, and MPS2(23/182.6) indicates that the MPS with the number 2 is accessed to the 23 node at the time and power is continuously supplied to the 182.6 time.
Table 4 scheduling timetable
Figure BDA0002859000480000171
Figure BDA0002859000480000181
The first-aid repair is completed at 1430.2 moment, no personnel materials and mobile emergency power generation cars are left unused in the period, full utilization is achieved, and meanwhile, the connection line scheme and the island division at each moment all achieve the maximum load which can be recovered currently.
Figure 4 is a graph comparing load recovery curves for whether rack reconstruction and islanding are considered,
the annealing convergence curve is shown in FIG. 5;
from the above, the curves show that the optimization method considering the net rack reconfiguration provided by the invention can remarkably improve the load recovery efficiency at the initial stage of emergency repair, can plan the specific route of emergency repair operation under the condition that a traffic network is damaged, and meanwhile, due to the existence of the connecting line, the load of a user can be recovered earlier, the total power loss is far less than that of the user, so that the post-disaster economic loss is greatly reduced, and the final scheme is reasonable and is closest to the optimal scheme.
The detailed adjustment scheme of the junctor, the distributed power supply, the personnel materials and the MPS is finally obtained by the upper and lower layer iterative solution method, the overall precision is improved to achieve the global optimal solution, and the iterative time is greatly reduced compared with a single intelligent algorithm.

Claims (10)

1. A power distribution network post-disaster first-aid repair decision method considering information fusion of a traffic network and a power distribution network is characterized by comprising the following steps:
step 1, acquiring non-electric quantity information of road congestion or interruption, and establishing an information fusion framework of a traffic network and a power distribution network, wherein the power distribution network maps disaster damage position information, material demand information and geographical coupling information of an MPS access point to the traffic network, and the traffic network feeds back fault point repair state information, warehouse material storage information and MPS available state information to the power distribution network;
step 2, calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
step 3, clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage quantity meets the requirement and the distance between the warehouses and the fault points is close;
step 4, under the precondition that the scheduling constraint, the material distribution constraint, the fault repair time constraint, the mobile emergency power generation vehicle constraint and the safety and stability operation constraint of a power distribution network emergency repair decision model are met, establishing a power distribution network emergency repair decision model based on grid reconstruction and island division;
and 5, solving the distribution network emergency repair decision model by adopting an upper-layer and lower-layer iterative solving method, solving the upper-layer model, the genetic algorithm and the Prim algorithm by adopting a simulated annealing algorithm to solve the lower-layer model, and solving to obtain an optimal distribution network emergency repair decision scheme.
2. The power distribution network post-disaster first-aid repair decision method considering information fusion of the traffic network and the power distribution network according to claim 1, wherein the information fusion framework of the traffic network and the power distribution network in the step 1 specifically comprises:
constructing a road traffic network according to the topological structure relationship of each road, and supporting a road network system by constructing a node set, a road grade set, a road section connection relationship, a disaster situation and length set, intersection delay and a road traffic capacity set;
the distribution network maps disaster position information, material demand information and MPS access point geographical coupling information to the traffic network, and meanwhile, the traffic network feeds back fault point repair state information, warehouse reserve information and MPS available state information to the distribution network.
3. The power distribution network post-disaster first-aid repair decision method considering information fusion of the traffic network and the power distribution network according to claim 1, wherein the step 2 of reducing the topological scale by using a Floyd algorithm comprises the following steps:
step 2-1: determining the equivalent distance between two adjacent points in the topology:
Figure RE-FDA0003002181870000011
in the formula (d)xyIs the distance between x and y, λxyThe vehicle speed correction coefficient is set according to the road type, and reflects the damage degree of the vehicle speed correction coefficient affected by typhoon disasters; alpha is alphaxyIs a variable, is 1 communicated from x to y, otherwise is 0, M is a maximum value, DxIndicating the delay time of intersection x, DyIndicating the delay time at intersection y;
step 2-2: and calculating the shortest path between any two fault points in the topology and the warehouse by using a Floyd algorithm, and if the two fault points are not connected indirectly, judging that the two fault points are disconnected.
4. The power distribution network post-disaster first-aid repair decision method considering information fusion of the traffic network and the power distribution network according to claim 1, wherein the clustering operation of the fault points and the warehouse in the traffic network in the step 3 specifically comprises:
matching the fault points with the warehouses by adopting spectral clustering, dividing the fault points into a plurality of clusters before path planning according to the number of the warehouses, wherein a clustering model is as follows:
Figure RE-FDA0003002181870000021
Figure RE-FDA0003002181870000022
Figure RE-FDA0003002181870000023
Figure RE-FDA0003002181870000024
in the formula, d (dep)σM) represents the distance of the warehouse dep belonging to the cluster σ from the failure point m; sσ,mThe variable is 0-1, the fault point m is allocated to the cluster sigma to be 1, otherwise, the cluster sigma is 0;
Figure RE-FDA0003002181870000025
representing the stock storage of the warehouse within the cluster sigma,
Figure RE-FDA0003002181870000026
representing the amount of material required by the fault point m;
after clustering, the same cluster only contains one warehouse and a plurality of fault points, and the fault points can only be accessed by construction teams and material vehicles in the same cluster.
5. The power distribution network post-disaster first-aid repair decision method considering information fusion of the traffic network and the power distribution network according to claim 1, wherein the power distribution network first-aid repair decision model in the step 4 specifically comprises:
the method comprises the following steps of determining a decision model objective function by taking the objective of reducing the load loss caused by the fault of the power distribution network as much as possible:
Figure RE-FDA0003002181870000027
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0003002181870000028
load weight of a node i of the power distribution network; rhoi,tIf the power distribution network node i is communicated at the moment t, the communication is 1, otherwise, the communication is 0;
Figure RE-FDA0003002181870000029
and carrying active load for the power distribution network node i at the moment t.
6. The power distribution network post-disaster rush-repair decision method considering information fusion of the traffic network and the power distribution network according to claim 5, wherein in step 4, the power distribution network rush-repair decision model is specifically defined by construction team scheduling constraint, material distribution constraint, fault repair time constraint, mobile emergency power generation vehicle constraint and power distribution network safe and stable operation constraint:
(1) construction team scheduling constraint:
the construction team arriving at the failure point also leaves from that point after the repair is completed:
Figure RE-FDA0003002181870000031
Figure RE-FDA0003002181870000032
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0003002181870000033
representing that the construction team c in the cluster sigma moves from m to n and is 1, otherwise, the construction team c is 0;
Figure RE-FDA0003002181870000034
representing that the construction team c in the cluster sigma moves from n to m, is 1, otherwise is 0; RC (resistor-capacitor) capacitorσRepresenting a construction team set; dp is warehouse node, NσA failure point and a warehouse point set are obtained;
the construction team starts from the warehouse:
Figure RE-FDA0003002181870000035
wherein the content of the first and second substances,
Figure RE-FDA0003002181870000036
representing that the construction team c in the cluster sigma moves from dp to m;
the construction team finally returns to the warehouse:
Figure RE-FDA0003002181870000037
wherein the content of the first and second substances,
Figure RE-FDA0003002181870000038
representing that the construction team c in the cluster sigma moves from m to dp, which is 1, otherwise, is 0; ncσThe number of construction teams in the cluster sigma;
any point of failure can only be accessed by one team:
Figure RE-FDA0003002181870000039
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000310
whether the construction team c visits the fault point m is 1, and if not, the fault point m is 0;
a construction team c arrives at n from m, then c must visit m:
Figure RE-FDA00030021818700000311
(2) and (3) material vehicle behavior constraint:
the total amount of materials allocated to the transport vehicle is not more than the amount of available materials of the warehouse in the cluster:
Figure RE-FDA00030021818700000312
in the formula (I), the compound is shown in the specification,
Figure RE-FDA00030021818700000313
is the actual carrying capacity of the vehicle v;
Figure RE-FDA00030021818700000314
(ii) a cluster sigma-inner bin capacity;
upper limit of material capacity of the transport vehicle v:
Figure RE-FDA00030021818700000315
in the formula, CapvIs the maximum carrying capacity of the vehicle v;
the transport vehicle arriving at the failure point leaves the point after delivering the material and moves to and from the point:
Figure RE-FDA0003002181870000041
Figure RE-FDA0003002181870000042
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0003002181870000043
indicating that the vehicle v is moving from m to dp is 1, otherwise it is 0;
Figure RE-FDA0003002181870000044
indicating that vehicle v is moving from dp to m, is 1, otherwise is 0; VEσA set of transport vehicles within cluster σ;
the transport vehicle starts from the warehouse and finally returns to the warehouse:
Figure RE-FDA0003002181870000045
Figure RE-FDA0003002181870000046
in the formula, nvσNumber of vehicles within cluster σ;
the minimum number of vehicles meeting the material requirement of a fault point m is as follows:
Figure RE-FDA0003002181870000047
Figure RE-FDA0003002181870000048
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0003002181870000049
the quantity of materials required for the fault point m;
Figure RE-FDA00030021818700000410
whether the transport vehicle v reaches the fault point m is represented as 1, and otherwise, the transport vehicle v is represented as 0;
if a vehicle v travels from m to dp, v must visit m:
Figure RE-FDA00030021818700000411
if the vehicle v travels from m to the next destination point n, v must visit m:
Figure RE-FDA00030021818700000412
Figure RE-FDA00030021818700000413
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000414
represents that the transport vehicle v moves from the fault point m to n, is 1, otherwise is 0;
time constraint for the vehicle to reach the next fault point n:
Figure RE-FDA00030021818700000415
Figure RE-FDA00030021818700000416
Figure RE-FDA0003002181870000051
Figure RE-FDA0003002181870000052
wherein, TPn,vThe time at which the vehicle v reaches the fault point n; TPm,vFor the time, tr, at which the vehicle v reaches the fault point mm,dp,vThe distance time, tr, for the fault m and the warehouse dpdp,n,vThe distance between the warehouse dp and the fault point n;
if the vehicle v reaches the failure point m once, the time of reaching the point is set to 0:
Figure RE-FDA0003002181870000053
Figure RE-FDA0003002181870000054
the material meeting time of the fault point m is the time of a transport vehicle arriving at the point at the latest:
Figure RE-FDA0003002181870000055
Figure RE-FDA0003002181870000056
(3) time constraint for fault repair:
construction team arrival time constraint:
Figure RE-FDA0003002181870000057
Figure RE-FDA0003002181870000058
wherein, ATm,c、TPm,vRepresenting the time of the construction team c and the transport vehicle v reaching the fault point m; ATn,cRepresenting the time when the construction team c reaches the fault point n; r ism,cTime required for maintenance of the fault point m for the construction team c; trm,n,cThe time required for construction team c to move from m to n;
vehicle arrival time constraints:
Figure RE-FDA0003002181870000059
Figure RE-FDA00030021818700000510
wherein, trm,dp,vRepresents the time required for the transport vehicle v to move from the trouble point m to the warehouse dp;
any failure point is repaired only once:
Figure RE-FDA00030021818700000511
wherein f ism,tWhether the fault point m is repaired at the moment t is represented as 1, and if not, the fault point m is 0;
maintenance starting time AP of construction teamm：
Figure RE-FDA00030021818700000512
Figure RE-FDA00030021818700000513
After the construction of the construction team is completed, the state of the fault point is set as 1:
Figure RE-FDA0003002181870000061
Figure RE-FDA0003002181870000062
Figure RE-FDA0003002181870000063
Figure RE-FDA0003002181870000064
if construction teamIf the fault point m is not visited, APmSetting 0:
Figure RE-FDA0003002181870000065
Figure RE-FDA0003002181870000066
the repaired failure point will be available in the following time:
Figure RE-FDA0003002181870000067
Figure RE-FDA0003002181870000068
wherein z ism,tIndicating whether the fault point m is available at the moment t, if so, the fault point m is 1, otherwise, the fault point m is 0;
when the point of failure state is not repaired, force
Figure RE-FDA0003002181870000069
Coupling construction team to distribution network constraints for 0:
Figure RE-FDA00030021818700000610
Figure RE-FDA00030021818700000611
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000612
indicating whether the line L on which the fault point m is located is available at time t, is 1,otherwise, the value is 0;
(4) restraint of the mobile emergency power generation vehicle:
MPS arriving at the access point also leaves this point after the power supply is over:
Figure RE-FDA00030021818700000613
Figure RE-FDA00030021818700000614
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000615
the fact that the power generation car s moves from the access point e to the access point f is 1, and otherwise, the number is 0;
Figure RE-FDA00030021818700000616
indicating that the generator car s moves from access point f to e, is 1, otherwise is 0, MσIs a set of access points;
MPS goes from warehouse and finally back to warehouse:
Figure RE-FDA00030021818700000617
Figure RE-FDA00030021818700000618
wherein the content of the first and second substances,
Figure RE-FDA0003002181870000071
indicating that the generator car s is moved from the warehouse dp to e, is 1, otherwise is 0;
Figure RE-FDA0003002181870000072
representing the movement of the generator car s from e to the warehouse dp,is 1, otherwise is 0, nsσNumber of MPS in a cluster, MPSσThe method comprises the steps of (1) collecting a generator car;
any MPS access point can only be accessed by one MPS:
Figure RE-FDA0003002181870000073
Figure RE-FDA0003002181870000074
wherein
Figure RE-FDA0003002181870000075
Indicating whether the power generation vehicle s accesses the access point e;
if the generator car s arrives at f from e, s must visit e.
Figure RE-FDA0003002181870000076
Figure RE-FDA0003002181870000077
Time of MPS arrival at next access point f:
Figure RE-FDA0003002181870000078
Figure RE-FDA0003002181870000079
Figure RE-FDA00030021818700000710
Figure RE-FDA00030021818700000711
in the formula, MPf,sFor the time of arrival of the Generation Car s at the Access Point f, MPe,sFor the time of arrival of the Generation Car s at Access Point e, tre,f,sTime, t, required for a generator car s to move from access point e to access point fe,sThe power supply time for the power generation cars s located at the access points e is continued;
MPS arrival, departure time constraint:
Figure RE-FDA00030021818700000712
Figure RE-FDA00030021818700000713
Figure RE-FDA00030021818700000714
Figure RE-FDA00030021818700000715
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000716
respectively indicating that the power generation car s arrives at and departs from the access point e at the time t, and if the access point e is 1, otherwise, the access point e is 0;
the availability status of the access point for MPS power duration is 1:
Figure RE-FDA00030021818700000717
Figure RE-FDA0003002181870000081
Figure RE-FDA0003002181870000082
wherein the content of the first and second substances,
Figure RE-FDA0003002181870000083
the state of the access point e is available when the generator car arrives and leaves, and is 1, otherwise, the state is 0; h ise,t,sIf the state in the power supply duration is available, the state is 1, otherwise, the state is 0;
(5) and (3) power distribution network safe and stable operation constraint:
MPS output constraint:
Figure RE-FDA0003002181870000084
Figure RE-FDA0003002181870000085
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0003002181870000086
the MPS is positioned at the active and reactive power output of a power distribution network node i at the moment t;
Figure RE-FDA0003002181870000087
the maximum active and reactive output is obtained; h ism,t,sThe MPS is represented as whether the MPS is available at the power distribution network node i at the moment t, and is 1, otherwise, the MPS is 0;
DG distributed power supply output constraint:
Figure RE-FDA0003002181870000088
Figure RE-FDA0003002181870000089
in the formula (I), the compound is shown in the specification,
Figure RE-FDA00030021818700000810
active and reactive power output of a DG at a node i of the power distribution network at the moment t;
Figure RE-FDA00030021818700000811
the maximum active and reactive output is obtained;
line power constraint:
Figure RE-FDA00030021818700000812
Figure RE-FDA00030021818700000813
wherein, Pk,t、Qk,tThe active power flow and the reactive power flow of the line k at the moment t are obtained;
Figure RE-FDA00030021818700000814
the upper limit of the active power and the reactive power of the line k;
Figure RE-FDA00030021818700000815
indicating whether the line k is available at the time t, and is 1, otherwise, is 0;
if line k is not switched or has a fault, its state is available:
Figure RE-FDA00030021818700000816
wherein SW is a line set with a switch, NL is a damaged line set;
and (3) power distribution network tree-shaped operation constraint:
Figure RE-FDA0003002181870000091
Figure RE-FDA0003002181870000092
Figure RE-FDA0003002181870000093
Figure RE-FDA0003002181870000094
Figure RE-FDA0003002181870000095
wherein, BS is a power supply set; beta is ai,j,tWhether the power distribution network node i is a father node of the power distribution network node j at the moment t is represented as 1, and if not, the number is 0; beta is aj,i,tWhether the power distribution network node j is a father node of the power distribution network node i at the moment t is represented as 1, and if not, the number is 0; nbtThe number of nodes of the power distribution network is;
branch flow constraint:
Figure RE-FDA0003002181870000096
Figure RE-FDA0003002181870000097
Figure RE-FDA0003002181870000098
Figure RE-FDA0003002181870000099
wherein the content of the first and second substances,
Figure RE-FDA00030021818700000910
the active and reactive requirements of the node i of the power distribution network are met; rhoi,tThe communication state of the loads carried by the nodes i of the power distribution network is 1, otherwise, the communication state is 0; k (, i) represents the current flowing into the distribution network node i branch set, and K (i,) represents the current flowing out of the distribution network node i branch set;
node voltage constraint:
Figure RE-FDA00030021818700000911
Figure RE-FDA00030021818700000912
wherein, Vi,tThe voltage of a power distribution network node i at the time t is obtained; rk、XkIs the impedance of line k; v1Is a reference voltage, VNIs a reference voltage;
and voltage deviation constraint:
Figure RE-FDA0003002181870000101
wherein epsilon takes 10%;
once the load i recovers at time t, it will be forced to remain connected for a later time:
Figure RE-FDA0003002181870000102
7. the power distribution network post-disaster first-aid repair decision method considering information fusion of the traffic network and the power distribution network according to claim 5, wherein the iterative solution algorithm in the step 5 specifically comprises the following steps:
step 5-1, solving a tie line action scheme by adopting a genetic algorithm; inputting parameters, randomly generating an initial population pop matrix consisting of 0 and 1, wherein the matrix scale is a multiplied by c, a is 100, namely, the initial population pop matrix represents that 100 chromosomes are contained in a population, the chromosomes are arrays with the length of c, c represents the number of links in a power distribution network, the number of the corresponding position in the chromosome is the number of the links, wherein 0 represents open, and 1 represents closed;
each chromosome represents a reconstruction scheme of a current rush-repair stage of a connecting line, all solutions in the pop need to meet radial operation constraint through depth-first search and verification, and meanwhile, branch flow constraint needs to be met, and if the solutions do not meet the branch flow constraint, a population needs to be regenerated;
step 5-2, solving a distributed power supply island division scheme by adopting a Prim algorithm; the distribution network topology contains 2 types of edges:
(1) the distribution network nodes are connected with the distribution network nodes to form edges;
(2) the distribution network nodes and the DG distributed power nodes are connected to form edges;
in the island searching process, edges formed by connecting power distribution network nodes and DG distributed power supply nodes are set to be 0, the priority is highest, the edges are searched first, the load-carrying power distribution network nodes can be determined through the load power and the load grade connected to the edges, the weight of the edges is determined to be actually multi-index quantitative evaluation, the load power and the load weight comprise 2 indexes, the weight can be obtained through a formula, and the edge weight is calculated by the formula:
Wd＝λ1PNiλ2Sz
Figure RE-FDA0003002181870000103
wherein, WdThe weight value of the d-th edge is; lambda [ alpha ]1、λ2The weight of the two indexes is the proportion of the two indexes in the weight; pmax、PminThe method comprises the steps of obtaining the maximum value and the minimum value of active load in a power distribution network needing to be subjected to islanding; pi、PNiThe active load value and the standard value of the ith power distribution network node are obtained; szIs the load grade;
5-3, solving the upper layer by adopting a simulated annealing algorithm; the cellular array coding is divided into two parts, namely a construction team path and a material vehicle path; such as
Figure RE-FDA0003002181870000111
The first matrix represents the first-aid repair scheme of a construction team, A and B represent warehouses, 0 or 1 represents whether a failure point is reached or not, and the position of the warehouse represents the number of the failure point;
step 5-4, after the repair time sequence of a certain fault point is obtained in step 5-3, and any fault point represents the change of a repaired or unrepaired repair state, calculating a grid frame reconstruction scheme according to the current power distribution network topology through the genetic algorithm in step 6-1 to obtain a grid frame structure required by lower island division;
5-5, after the reconstruction scheme obtained in the step 5-4 is implemented, searching DG nodes existing in the power distribution network on the basis of the current network frame topology, solving an optimal island division strategy under the reconstruction scheme by adopting the Prim algorithm in the step 5-2, and calculating the load recovery amount of a lower layer;
and 5-6, disturbing the personnel material scheduling scheme on the upper layer according to the optimization rule of the simulated annealing algorithm in the step 5-3, wherein the disturbance method comprises two-point exchange, two-section exchange and point-section exchange, the disturbance method still is a feasible solution meeting the path planning constraint condition after disturbance, then repeating the steps 5-4 and 5-5 to calculate an objective function value, and finally obtaining the optimal power distribution network emergency maintenance decision scheme through outer layer iteration.
8. The utility model provides a take into account distribution network disaster recovery decision-making system of traffic network and distribution network information fusion which characterized in that includes following module:
the two-network information fusion module: the system comprises a traffic network and power distribution network information fusion framework, a power distribution network information fusion framework and a power distribution network information fusion framework, wherein the traffic network and the power distribution network information fusion framework are used for collecting non-electric quantity information of road congestion or interruption under typhoon disasters;
and (3) shortest path calculation: calculating the shortest path between the traffic gateway key nodes by using a Floyd algorithm, and reducing the topological scale to only contain key nodes;
rush-repair target clustering module: clustering the fault points and the warehouses in the traffic network according to the principle that the warehouse material storage quantity meets and is close to the distance between the fault points;
the power distribution network emergency repair decision model building module comprises: the system is used for establishing a power distribution network emergency repair decision model based on network frame reconstruction and island division, and meeting construction team scheduling constraint, material distribution constraint, fault repair time constraint, mobile emergency power generation vehicle constraint and power distribution network safe and stable operation constraint conditions of the power distribution network emergency repair decision model;
a model solving module: and solving the power distribution network emergency repair decision model by adopting an upper-layer iteration solving method and a lower-layer iteration solving method, and solving an optimal power distribution network emergency repair decision scheme.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented by the processor when executing the computer program.
10. A computer-storable medium having a computer program stored thereon, wherein the computer program is adapted to carry out the steps of the method according to any one of claims 1-7 when executed by a processor.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113487132A (en) * 2021-06-02 2021-10-08 广东电网有限责任公司广州供电局 Distribution network post-disaster first-aid repair resource allocation method and device and computer equipment
CN113537703A (en) * 2021-06-04 2021-10-22 广东电网有限责任公司广州供电局 Power distribution network pre-disaster emergency resource deployment method and device and computer equipment
CN114004550A (en) * 2022-01-04 2022-02-01 山东大学 Power grid emergency repair and restoration scheduling cooperative decision method and system under natural disaster
CN115713325A (en) * 2023-01-09 2023-02-24 佰聆数据股份有限公司 Power line repair construction operation duration analysis method and device
CN116937580A (en) * 2023-09-19 2023-10-24 华北电力大学 Zone-tunnel power supply system elastic recovery method based on direct-current line interconnection

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107332234A (en) * 2017-06-30 2017-11-07 燕山大学 One kind considers the intermittent active distribution network multiple faults correcting strategy of regenerative resource
CN108053037A (en) * 2017-10-16 2018-05-18 广东电网有限责任公司电力科学研究院 A kind of power distribution network based on two net fusions repairs policy development method and device
CN110852627A (en) * 2019-11-13 2020-02-28 国电南瑞科技股份有限公司 Decision method and device for post-disaster first-aid repair of power distribution network
CN111539566A (en) * 2020-04-21 2020-08-14 燕山大学 Power distribution network multi-fault first-aid repair recovery method and system considering pre-scheduling before disaster

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107332234A (en) * 2017-06-30 2017-11-07 燕山大学 One kind considers the intermittent active distribution network multiple faults correcting strategy of regenerative resource
CN108053037A (en) * 2017-10-16 2018-05-18 广东电网有限责任公司电力科学研究院 A kind of power distribution network based on two net fusions repairs policy development method and device
CN110852627A (en) * 2019-11-13 2020-02-28 国电南瑞科技股份有限公司 Decision method and device for post-disaster first-aid repair of power distribution network
CN111539566A (en) * 2020-04-21 2020-08-14 燕山大学 Power distribution network multi-fault first-aid repair recovery method and system considering pre-scheduling before disaster

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113487132A (en) * 2021-06-02 2021-10-08 广东电网有限责任公司广州供电局 Distribution network post-disaster first-aid repair resource allocation method and device and computer equipment
CN113537703A (en) * 2021-06-04 2021-10-22 广东电网有限责任公司广州供电局 Power distribution network pre-disaster emergency resource deployment method and device and computer equipment
CN114004550A (en) * 2022-01-04 2022-02-01 山东大学 Power grid emergency repair and restoration scheduling cooperative decision method and system under natural disaster
CN115713325A (en) * 2023-01-09 2023-02-24 佰聆数据股份有限公司 Power line repair construction operation duration analysis method and device
CN116937580A (en) * 2023-09-19 2023-10-24 华北电力大学 Zone-tunnel power supply system elastic recovery method based on direct-current line interconnection
CN116937580B (en) * 2023-09-19 2023-11-28 华北电力大学 Zone-tunnel power supply system elastic recovery method based on direct-current line interconnection

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