CN114389263A - Elastic power distribution network post-disaster recovery method and system based on information physical collaborative optimization - Google Patents

Elastic power distribution network post-disaster recovery method and system based on information physical collaborative optimization Download PDF

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CN114389263A
CN114389263A CN202210073634.4A CN202210073634A CN114389263A CN 114389263 A CN114389263 A CN 114389263A CN 202210073634 A CN202210073634 A CN 202210073634A CN 114389263 A CN114389263 A CN 114389263A
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陈健
孙秀川
赵浩然
张文
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Shandong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The utility model belongs to the technical field of distribution network elastic lifting, and provides an elastic distribution network post-disaster recovery method and system based on information physical collaborative optimization, which comprises the following steps: acquiring post-disaster physical information of the elastic power distribution network; according to the acquired information and a preset recovery model, the post-disaster recovery of the power distribution network is realized; the recovery model comprises a first-stage recovery submodel and a second-stage recovery submodel, wherein the first-stage recovery submodel takes the minimum loss load after the fault occurs as a target function and takes the scheduling path constraint of maintenance personnel, the time unification constraint of parallel maintenance personnel and the network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after the load is completely recovered and the time unified constraint of the maintenance personnel and the scheduling path constraint of the maintenance personnel as constraint conditions.

Description

Elastic power distribution network post-disaster recovery method and system based on information physical collaborative optimization
Technical Field
The disclosure belongs to the technical field of distribution network elastic lifting, and particularly relates to an elastic distribution network post-disaster recovery method and system based on information physical collaborative optimization.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Although the development of power grid intellectualization is accelerated by continuously deepening the coupling degree of the information network and the physical network, the external environment of the electric power information physical system is more complex, because the communication network of the electric power system adopts a mode of an optical fiber composite overhead ground wire, the simultaneous multi-point fault is easy to occur, and the fault of the communication network causes the corresponding disaster area to be not observable and uncontrollable, the key remedial service cannot be issued, and the fault range is further expanded. Therefore, it is important to consider the coupling effect between the communication network and the power network and establish an effective recovery model so that the user can recover the power supply in time.
According to the inventor, the existing research on the recovery strategy after the distribution network disaster mostly only considers the single physical side fault recovery, and does not consider the influence of the information system fault on the power system recovery. The information physical line is usually laid in the same architecture mode, so that the situation that multipoint simultaneous faults of information physics are likely to occur after natural disasters occur is determined, normal actions of physical components are affected, and the single strategy of considering physical side recovery is not applicable any more along with the continuous improvement of the intelligent level of a power grid. In addition, in the recovery strategy under the influence of physical coupling of information, the road travel time of the maintenance personnel plays a considerable role, so it is important to consider the coupling characteristics of the information network, the physical network and the traffic network as shown in fig. 2 to guide the maintenance personnel to select the optimal path to reach the fault point and to perform the repair action.
Disclosure of Invention
In order to solve the problems, the disclosure provides an elastic distribution network post-disaster recovery method and system based on information physical collaborative optimization, coupling influence of an information system and a physical system is considered, a fault element is repaired through collaborative scheduling of a primary maintenance worker and a secondary maintenance worker, rapid post-disaster recovery of a distribution network is achieved, and distribution network elasticity is improved.
According to some embodiments, a first scheme of the present disclosure provides an elastic distribution network post-disaster recovery method based on information physical collaborative optimization, and the following technical scheme is adopted:
an elastic power distribution network post-disaster recovery method based on information physical collaborative optimization comprises the following steps:
acquiring post-disaster physical information of the elastic power distribution network;
according to the acquired information and a preset recovery model, the post-disaster recovery of the power distribution network is realized;
the recovery model comprises a first-stage recovery sub-model and a second-stage recovery sub-model, wherein the first-stage recovery sub-model performs post-disaster network reconstruction of the power distribution network by taking the minimum loss load after a fault occurs as a target function and taking scheduling path constraint of maintenance personnel, time unified constraint of parallel maintenance personnel and network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after all the loads recover power supply as a target function and takes the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions to repair the fault elements of the reconstructed power distribution network after the disaster and perform the cooperative recovery of the power distribution network after the disaster.
As a further technical limitation, in the first stage of recovery process, the primary repair is matched with the secondary repair and the network reconstruction, and the primary maintenance personnel and the secondary maintenance personnel are scheduled and arranged and carry out a reasonable network reconstruction strategy; in the second stage of recovery process, the primary maintenance personnel and the secondary maintenance personnel are rescheduled and arranged, and the primary maintenance personnel and the secondary maintenance personnel are matched to quickly recover the fault element.
As a further technical limitation, the restoration model is a distribution network restoration model with a variable time step triggered based on a restoration event, and specifically includes:
each time, information element repair activity and physical element repair activity are regarded as a repair event to trigger a network reconstruction judgment, when two or more repair events are completed at the same time, the repair events can be regarded as different repair times with the time interval of 0 to trigger the network reconstruction, and the load loss after the disaster is reduced through the coordinated operation of the first repair, the second repair and the network reconstruction.
As a further technical limitation, the dispatch path constraint of the serviceman is: and (3) restraining the repair paths, repair sequences and repair resources of the primary and secondary maintainers, establishing a complete path model for repairing the fault element by a plurality of maintainers in parallel, and setting a path decision variable corresponding to a path at the repair time to be 0 corresponding to the maintainers not completing the repair activity if the maintainers complete the repair activity at different fault times.
As a further technical limitation, the time unification constraint of the parallel maintenance personnel is: according to the relation between the repair time and the repair path, firstly, the repair time sequences corresponding to the individual repair activities of different maintenance teams are established, the repair time of other repair teams not completing the repair activities at a certain repair moment is set to be 0 through the repair activity flag bit, then the repair activities of all teams are unified, the sequences corresponding to the real repair activities and arranged according to the sequence of the occurrence of the events are obtained, and the subsequent triggering of network reconstruction judgment is facilitated.
As a further technical limitation, the objective function of the first-stage recovery submodel is min Σk∑i(1-δi,k)*p_loadi*(Δtk) Wherein, deltai,tkFor the load recovery coefficient, p _ load, of load node i after the kth repair activity is completed and reconstructediIs the active power of the load node i, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
As a further technical limitation, the objective function of the second-stage recovery submodel is min ΣkΔtkWherein, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
According to some embodiments, a second scheme of the present disclosure provides an elastic distribution network post-disaster recovery system based on information physical collaborative optimization, and the following technical scheme is adopted:
the utility model provides an elasticity distribution network recovery system after disaster based on information physics collaborative optimization, includes:
an acquisition module configured to acquire post-disaster physical information of an elastic distribution network;
the post-disaster recovery module is configured to realize post-disaster recovery of the power distribution network according to the acquired information and a preset recovery model;
the recovery model comprises a first-stage recovery sub-model and a second-stage recovery sub-model, wherein the first-stage recovery sub-model performs post-disaster network reconstruction of the power distribution network by taking the minimum loss load after a fault occurs as a target function and taking scheduling path constraint of maintenance personnel, time unified constraint of parallel maintenance personnel and network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after all the loads recover power supply as a target function and takes the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions to repair the fault elements of the reconstructed power distribution network after the disaster and perform the cooperative recovery of the power distribution network after the disaster.
According to some embodiments, a third aspect of the present disclosure provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium, on which a program is stored, which, when being executed by a processor, implements the steps in the resilient power distribution network after-disaster recovery method based on cyber-physical collaborative optimization according to the first aspect of the present disclosure.
According to some embodiments, a fourth aspect of the present disclosure provides an electronic device, which adopts the following technical solutions:
an electronic device includes a memory, a processor, and a program stored on the memory and executable on the processor, where the processor executes the program to implement the steps in the resilient power distribution network after-disaster recovery method based on cyber-physical collaborative optimization according to the first aspect of the disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
the influence of the coupling characteristic of information physics on the elasticity improvement of the power distribution network after the disaster is comprehensively considered, and the load is recovered more effectively by combining network reconstruction and scheduling the cooperative scheduling arrangement of primary maintenance personnel and secondary maintenance personnel, so that the provided strategy is more in line with the development of the automation level of the power distribution network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a method for recovering an elastic power distribution network after disaster based on information physical collaborative optimization in a first embodiment of the present disclosure;
FIG. 2 is a diagram of a distribution information network coupled to a physical network and a traffic network in accordance with the present disclosure;
FIG. 3 is a schematic diagram of a first stage of a single physical side recovery strategy in a first embodiment of the disclosure;
FIG. 4 is a diagram illustrating a second phase of a single physical-side recovery policy in a first embodiment of the disclosure;
fig. 5 is a diagram of a single physical side recovery strategy considering information influence in a first embodiment of the disclosure;
FIG. 6 is a schematic diagram of a first phase of a collaborative recovery strategy of an cyber-physical system in a first embodiment of the disclosure;
FIG. 7 is a diagram illustrating a second phase of a collaborative recovery policy of an cyber-physical system in a first embodiment of the disclosure;
fig. 8 is a block diagram of a post-disaster recovery system of an elastic distribution network based on information physical collaborative optimization in the second embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example one
The first embodiment of the disclosure introduces an elastic distribution network post-disaster recovery method based on information physical collaborative optimization.
The method for recovering the elastic distribution network after the disaster based on the information physical collaborative optimization, as shown in fig. 1, comprises the following steps:
a two-stage recovery model of information physical system cooperative recovery is established:
in the first stage of recovery process, the primary repair, the secondary repair and the network reconstruction are matched, and the primary maintenance personnel and the secondary maintenance personnel are scheduled and arranged and carry out a reasonable network reconstruction strategy;
respectively establishing scheduling path constraints of maintenance personnel, paralleling time unified constraints of the maintenance personnel and network reconstruction constraints under the influence of information physical coupling by taking the minimum loss load after the fault occurs as a target function, and establishing a first-stage collaborative recovery model;
in the second stage recovery process, the primary maintenance personnel and the secondary maintenance personnel are rescheduled and arranged and are matched with each other to quickly recover the fault element;
and establishing a second-stage cooperative recovery model by taking the minimum time cost for repairing the residual fault element after the load is completely restored to supply power as an objective function and taking the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions.
As one or more embodiments, a distribution network recovery model with variable time step triggered based on a repair event is established, specifically:
each time, information element repair activity and physical element repair activity are regarded as a repair event to trigger a network reconstruction judgment, when two or more repair events are completed at the same time, the repair events can be regarded as different repair times with the time interval of 0 to trigger the network reconstruction, and the load loss after the disaster is reduced through the coordinated operation of the first repair, the second repair and the network reconstruction.
As one or more embodiments, the first-stage power distribution network restoration objective function is specifically:
min∑k∑i(1-δi,k)*p_loadi*(Δtk)
wherein, deltai,tkFor the load recovery coefficient, p _ load, of load node i after the kth repair activity is completed and reconstructediIs the active power of the load node i, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
As one or more embodiments, the second-stage power distribution network restoration objective function is specifically:
min∑kΔtk
wherein, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
As one or more implementation modes, establishing a parallel scheduling constraint condition between a primary maintenance person and a secondary maintenance person specifically comprises the following steps:
and (3) restraining the repair paths, repair sequences and repair resources of the primary and secondary maintainers, establishing a complete path model for repairing the fault element by a plurality of maintainers in parallel, and setting a path decision variable corresponding to a path at the repair time to be 0 corresponding to the maintainers not completing the repair activity if the maintainers complete the repair activity at different fault times.
As one or more implementation modes, a time unified model of multiple parallel teams is established, the relationship between repair time and repair paths is combined, firstly, repair time sequences corresponding to the independent repair activities of different maintenance teams are established, the repair time of the repair teams with other incomplete repair activities at a certain repair moment is set to be 0 through a repair activity flag bit, then, the repair activities of all the teams are unified, a sequence which is arranged according to the sequence of event occurrence and is corresponding to the real repair activities is obtained, and the network reconfiguration judgment is convenient to trigger subsequently.
Further, a power distribution network reconstruction model considering the information physical coupling influence is established, the fact that a corresponding physical area is invisible and uncontrollable after an information node is invalid and the information node needs a physical node function for normal work and the like is considered, an information physical coupling constraint condition is established and embedded into a power distribution network reconstruction constraint to obtain a network reconstruction constraint considering the information physical coupling influence, wherein the information physical coupling constraint is specifically as follows:
Figure BDA0003483031470000091
Figure BDA0003483031470000092
Figure BDA0003483031470000093
wherein the content of the first and second substances,
Figure BDA0003483031470000101
the actual working state of the reconstructed g-number distribution line after the k-th repair activity is represented, if the actual working state is closed, the actual working state is 1, otherwise, the actual working state is 0,
Figure BDA0003483031470000102
indicating the fault state of the g-number distribution line after the k-th repair action, ifA 1 indicates that no failure has occurred or that the failure is repaired.
Figure BDA0003483031470000103
The working state of the ith information node after the kth repairing activity is shown, the normal work is 1, otherwise, the normal work is 0,
Figure BDA0003483031470000104
for the power that the power supply node sends out after the kth repair activity,
Figure BDA0003483031470000105
the upper power limit for the power supply node.
As one or more embodiments, a network reconfiguration idea-based information node connection communication judgment condition is provided, and a specific model thereof is as follows:
Figure BDA0003483031470000106
Figure BDA0003483031470000107
Figure BDA0003483031470000108
Figure BDA0003483031470000109
Figure BDA00034830314700001010
Figure BDA00034830314700001011
Figure BDA00034830314700001012
wherein N iscAnd LcRespectively representing the information nodes and the communication link set, M is a maximum number, N is the total number of the information nodes,
Figure BDA00034830314700001013
c_nodeirepresents an inode load, having a value of 1;
Figure BDA00034830314700001014
indicating the "power" that the central control node sends out after the kth repair activity,
Figure BDA00034830314700001015
represents the 'power' flowing on the information link after the kth repair activity;
Figure BDA00034830314700001016
in order to obtain the working state of the reconstructed link,
Figure BDA00034830314700001017
is a flow of goods in a topological constraint; if the information node is judged by network reconstruction, the power supply can be recovered, namely the load recovery coefficient
Figure BDA00034830314700001018
If the number of the information subnodes is 1, indicating that an available path exists between the information subnodes and the central control node so that the connectivity between the information subnodes and the central control node is kept, and at the moment, the information subnodes can normally work; if the power supply of the information subnode is not recovered after the reconstruction judgment, the information subnode indicates that no available path exists between the information subnode and the central control node, namely the load recovery coefficient
Figure BDA0003483031470000111
And if the value is 0, the connectivity between the information child node and the central control node is lost, and the child node cannot work normally.
The embodiment provides an elastic power distribution network post-disaster recovery method based on information physics collaborative optimization, which is based on an elastic power distribution network post-disaster two-stage recovery strategy of information physics collaborative optimization, and establishes the following model:
1. maintenance personnel path scheduling model
The number of times of repair can be obtained according to the number of the fault elements of the cyber physical system, that is, the number of times of repair exists when the number of the fault elements of the cyber physical system needs to be repaired, and each time of repair corresponds to one time of network reconstruction judgment. Therefore, in the embodiment, the repair team dispatching path model and the repair and road travel time model are integrated together, and finally the repair path and each repair time model of the repair team are obtained. The method comprises the following specific steps:
in the repair scheduling model, MpAnd McRepresenting sets of physical and information maintenance teams, M, respectivelypAnd McRespectively comprising a physical repair team and an information repair team (mp)i，mci)，NFpAnd NFcRepresenting sets of vertices in a connectivity graph of information and physical networks, EpAnd EcRepresenting a set of edges in a connectivity graph, the physical system vertices (denoted f, g) include a repository of physical repair teams (denoted f, g)vpo) And a damaged physical element, the information system vertex (denoted l, h) comprising a repository of information repair teams (denoted l, h)vco) And a corrupted information element, an edge representing an available path connecting two vertices. T isfRepresents a failure repair time set including a failure repair sequence number and a time (k, t) corresponding to the failure repairk)，
Figure BDA0003483031470000112
Is a variable from 0 to 1, indicating whether the kth repair event is mpiPhysical repair team (information repair team mc)i) Starting from the starting point, arriving at the physical fault line with the number g (information fault link with the number h) and performing repair action, wherein if the physical fault line with the number h is 1, the physical fault line with the number g starts from the starting point and then at the time tkAnd (4) repairing when the fault repairing time reaches a fault point, otherwise, the fault point is 0.
Figure BDA0003483031470000121
Indicates whether the kth repair event is mpiPhysical repair team (information repair team mc)i) Starting from a fault point f (l), arriving at a physical fault line (information fault link) No. g and performing repair action, and if the number is 1, indicating that the fault point f (l) starts and then at tkAnd (4) repairing when the fault repairing time reaches a fault point, otherwise, the fault point is 0. Based on the method, the repair path model of the physical and information maintenance team is characterized as follows:
Figure BDA0003483031470000122
Figure BDA0003483031470000123
the above formula indicates that each maintenance team needs to start repair activities from the warehouse, if the left formula is equal to 1, the corresponding maintenance team is assigned with a repair task, and when the maintenance team is larger than the number of faults or a certain maintenance team is too far away from any fault element, a situation that repair resources are idle may occur, that is, the corresponding maintenance team does not perform repair activities, and the corresponding formula is equal to 0;
since each fault repair event corresponds to one fault repair time, only one faulty component is repaired at each repair time, and in addition, if a situation occurs in which a plurality of faulty components are repaired at the same time, this embodiment regards them as a plurality of different fault repair times with a time interval of 0.
Figure BDA0003483031470000124
For a physical system failure point, one and only one repair team completes the repair of the failed element at a certain failure repair time, and the same is true for the information system failure link:
Figure BDA0003483031470000125
Figure BDA0003483031470000131
similarly, after the repair of a failed component is completed, there can be at most one repair team leaving the component to the next failure point:
Figure BDA0003483031470000132
Figure BDA0003483031470000133
for the first time of fault repair, the corresponding repair team must arrive at the fault point from the warehouse position for repair, and the mathematical description can be expressed as:
Figure BDA0003483031470000134
furthermore, for a certain repair team, if it is at tkAt the moment a repair action is performed on a faulty component, the next repair action must be to go from the faulty component to another faulty component and to perform the repair action. That is, since different repair teams repair the faulty component in parallel in time, in order to keep the path models of the individual repair teams coherent, the following is depicted:
Figure BDA0003483031470000135
Figure BDA0003483031470000136
the above formula shows that at tkMoment if there is a variable
Figure BDA0003483031470000137
1, the t-thkAt time point, the repair team reaches point g from point f and repairs, then at tkAt the previous time point, the team must arrive at the point f from other points for repairing at the last repairing time, and so on, the continuous repairing path model of each repairing team can be obtained.
2. Time unification model
Different repair teams are in a parallel repair relation in time, however, the repair teams trigger a power distribution network recovery model as events, and the coordination and cooperation among the repair teams and the repair teams are critical to the recovery of the power distribution network. In order to unify different repair teams on a time axis for triggering a network reconfiguration model, the repair time and road travel time problem of the repair teams are modeled as follows:
in this model, the shortest path travel time taken by a physical repair team to reach another failure point g from a failure point f is used
Figure BDA0003483031470000141
Indicating, for physical failed node repair time
Figure BDA0003483031470000142
Indicating the time from leaving from f to g fault and performing a repair action to leaving from g fault after the fault is repaired
Figure BDA0003483031470000143
The relationship between the road trip time available to a repair team repair activity and the repair time of a faulty component is shown as follows:
Figure BDA0003483031470000144
Figure BDA0003483031470000145
wherein the content of the first and second substances,
Figure BDA0003483031470000146
the traffic travel time required for the shortest path between failure points can be derived by the Floyd algorithm.
Figure BDA0003483031470000147
Figure BDA0003483031470000148
Wherein, tk,mpi(tk,mci) As a decision variable, when tkThe moment of repair is mpiWhen the repair team performs the repair, tk,mpi(tk,mci) Is equal to mpiThe time for the repair team to leave from the last repaired fault element to reach the fault element to be repaired and repair the corresponding fault element is up, if tkThe moment is the first repair action of the team, then tk,mpi(tk,mci) Equal to the time it takes to arrive at the point of failure from the warehouse location and to repair it. For other maintenance teams, the corresponding scheduling path decision variable
Figure BDA0003483031470000149
Is 0, so t corresponds tok,mpi(tk,mci) Is 0.
Figure BDA0003483031470000151
Figure BDA0003483031470000152
Figure BDA0003483031470000153
For repairing the markA flag for determining at tkWhether the corresponding repair team finishes the repair activities at the repair finishing time or not, if the repair team finishes the repair, the repair team corresponding to the repair team finishes the repair activities
Figure BDA0003483031470000154
Is 1, otherwise is 0.
Tk,mpi＝βtk,mpi*∑mtm,mpi,m＝1,2,...k
Figure BDA0003483031470000155
tk real＝∑mpiTk,mpi+∑mciTk,mci
The physical time corresponding to the k-th repair activity is the sum of the k time intervals before the k-th repair activity. For other maintenance teams, at tkNo repair action is performed at all times, so that the flag bit beta is increasedtk,mpi(βtk,mci) So that at tkTime series T for the completion of the actual per team repairk,mpi(or T)k,mci) Is 0. At the moment, the real physical time corresponding to different maintenance teams is added to obtain a time sequence t that the parallel teams can uniformly repair the fault elements to the same time axisk real。
Δtk＝tk real-tk-1 real
Thereby obtaining the time interval delta t of triggering the network reconstruction by each repairing eventkAs shown in the above formula. In addition, in order to ensure that different repair teams are ordered according to the time sequence when unified on a time axis, constraint conditions are added: Δ tk≥0。
3. Distribution network reconstruction model under information physical coupling
Figure BDA0003483031470000156
Figure BDA0003483031470000157
Figure BDA0003483031470000158
Wherein the content of the first and second substances,
Figure BDA0003483031470000161
the actual working state of the reconstructed No. g distribution line after the kth repairing activity is represented, if the actual working state is closed, the actual working state is 1, otherwise, the actual working state is 0,
Figure BDA0003483031470000162
and the fault state of the distribution line of the number g after the k-th repair action is shown, and if the fault state is 1, the fault does not occur or the fault is repaired.
Figure BDA0003483031470000163
The working state of the ith information node after the kth repairing activity is shown, the normal work is 1, otherwise, the normal work is 0,
Figure BDA0003483031470000164
for the power supply node at tkThe power delivered after the moment of secondary repair,
Figure BDA0003483031470000165
the upper power limit for the power supply node.
For the network reconfiguration model, the DistFlow model is adopted to constrain the distribution network flow in this embodiment:
Figure BDA0003483031470000166
Figure BDA0003483031470000167
Figure BDA0003483031470000168
Figure BDA0003483031470000169
Figure BDA00034830314700001610
Figure BDA00034830314700001611
Figure BDA00034830314700001612
Figure BDA00034830314700001613
Figure BDA00034830314700001614
Figure BDA00034830314700001615
Figure BDA00034830314700001616
wherein the content of the first and second substances,
Figure BDA00034830314700001617
Ncthe method comprises the steps of (1) collecting nodes in a power distribution network; l ispA line set in the power distribution network is obtained;
Figure BDA00034830314700001618
and
Figure BDA00034830314700001619
at t, the transformer station respectively being node ikActive power and reactive power output at any moment;
Figure BDA00034830314700001620
and
Figure BDA00034830314700001621
distributed power sources, respectively nodes i, at tkActive power and reactive power output at any moment; deltai,tkFor node i at tkA load recovery factor at a time;
Figure BDA00034830314700001622
and
Figure BDA00034830314700001623
respectively, the line g is at tkReal power and reactive power flow in the line at the moment;
Figure BDA0003483031470000171
and
Figure BDA0003483031470000172
respectively, node i at tkThe real demand load and the reactive demand load of the moment;
Figure BDA0003483031470000173
for node i at tkVoltage amplitude at a time; r isgAnd xgRespectively, the resistance and reactance of the line g, and M is a positive number which is large enough; u shapemax、UminRespectively the minimum value and the maximum value of the node voltage in the power distribution network;
Figure BDA0003483031470000174
and
Figure BDA0003483031470000175
maximum apparent power and minimum apparent power on line g;
Figure BDA0003483031470000176
and
Figure BDA0003483031470000177
the maximum active power and the minimum active power of the distributed power supply installed for the node i respectively.
In order to ensure that the power distribution network still has a radial topological structure after reconstruction, a single commodity flow model is adopted to model the topology, wherein,
Figure BDA0003483031470000178
is tkCommodity flow of commodities on a g line at any moment; i.e. iDGInstalling nodes for the distributed power supply; wiThe total amount of commodities which can be sent by the source node in the sub-distribution network; m is a sufficiently large positive number, NDGFor the number of power sources contained in the power distribution network, N is the number of information nodes:
Figure BDA0003483031470000179
Figure BDA00034830314700001710
Figure BDA00034830314700001711
Figure BDA00034830314700001712
Figure BDA00034830314700001713
4. information node connectivity judgment model
Whether the information node can normally work depends on whether the node is communicated with the central control node or not, and therefore, based on the idea of distribution network reconstruction, the embodiment provides a modified single commodity flow model, the central control node is taken as a power supply node, the sub-nodes are taken as 1 load nodes, the network reconstruction model is adopted to judge whether the information sub-nodes are cut off or not, and if the information sub-nodes are cut off, the information sub-nodes are not communicated with the central control node. The concrete model is as follows:
Figure BDA0003483031470000181
the constraints are as follows:
Figure BDA0003483031470000182
Figure BDA0003483031470000183
Figure BDA0003483031470000184
Figure BDA0003483031470000185
Figure BDA0003483031470000186
Figure BDA0003483031470000187
wherein, c _ nodeiRepresents an inode load, having a value of 1;
Figure BDA0003483031470000188
indicating that the central control node is at tkThe "power" emitted after the moment,
Figure BDA0003483031470000189
represents tkThe 'power' flowing on the time information link,
Figure BDA00034830314700001810
in order to obtain the working state of the reconstructed link,
Figure BDA00034830314700001811
is a flow of goods in a topological constraint. If the information node is judged by network reconstruction, the power supply can be recovered, namely the load recovery coefficient
Figure BDA00034830314700001812
If the number of the information subnodes is 1, indicating that an available path exists between the information subnodes and the central control node so that the connectivity between the information subnodes and the central control node is kept, and at the moment, the information subnodes can normally work; if the power supply of the information subnode is not recovered after the reconstruction judgment, the information subnode indicates that no available path exists between the information subnode and the central control node, namely the load recovery coefficient
Figure BDA00034830314700001813
And if the value is 0, the connectivity between the information child node and the central control node is lost, and the child node cannot work normally.
5. Linearization process
The existence of the constraint of the time unified model is a nonlinear model, and in order to convert the model into a linear model, the embodiment introduces an auxiliary variable
Figure BDA00034830314700001814
The following formula is processed:
Tk,mpi＝βtk,mpi*∑mtm,mpi,m＝1,2,...k
Figure BDA0003483031470000191
order to
Figure BDA0003483031470000192
The following can be obtained:
Figure BDA0003483031470000193
wherein
Figure BDA0003483031470000194
The following constraints are satisfied:
Figure BDA0003483031470000195
in the same way, order
Figure BDA0003483031470000196
The following can be obtained:
Figure BDA0003483031470000197
Figure BDA0003483031470000198
because the first-stage model objective function contains
Figure BDA0003483031470000199
ΔtkTwo decision variables, making the model a MIQP problem, so auxiliary variables are introduced
Figure BDA00034830314700001910
The original objective function is linearized and added with connectivity judgment constraint, so that the original objective function becomes:
Figure BDA00034830314700001911
and the following constraint conditions are satisfied:
Figure BDA00034830314700001912
where ζ is a suitable decimal number.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific simulation examples and comparative examples.
Fig. 3 and 4 are schematic diagrams of a two-stage restoration strategy considering only a single physical side, and when the restoration influence of an information fault on the power system is not considered, the system can be known to restore the full load power supply after 295min, and then reschedule the restoration team with the aim of minimizing the time cost for restoring the remaining faulty element until the faulty element is completely restored after 989 min. However, in the face of natural disasters, a power distribution network communication link is also very prone to failure, and a communication network failure causes an invisible and uncontrollable power grid in a corresponding area, and affects actions such as a remote control switch, so that load recovery is affected, fig. 5 is a recovery strategy diagram of the power distribution network under the influence of an information system failure, and is known by comparing with a single physical network recovery strategy, when the information network fails simultaneously, a gray node on the right side of the information network loses connectivity with a central control node, so that the corresponding physical area is uncontrollable, when a 31-33 line is repaired, and when the remaining failed line is repaired, the line cannot be remotely controlled to be closed, so that loads of nodes 8-18 and 21-22 cannot be recovered, and if all loads are to be recovered, power supply can be recovered only by considering other measures subsequently.
The above-mentioned faults are simulated according to the recovery strategy proposed in this embodiment, the repair paths and the power supply recovery sequence of the primary maintenance personnel and the secondary maintenance personnel are calculated and obtained as shown in fig. 6 and 7, the power supply sequence of each node in the power distribution network is represented by solid lines with different colors, and the power supply paths and the power supply recovery time of the nodes are indicated. After a fault occurs, firstly carrying out network reconstruction, wherein loads 1-8 and loads 19 and 20 can be normally powered, then respectively dispatching primary maintenance personnel and secondary maintenance personnel to repair a fault element, after 169min, repairing lines 3-23 and closing the lines and lines 5-29 through network reconstruction, so that the loads 23-25 and loads 28-31 are powered again, information links 15-16 are repaired to 191min, the connectivity of the information nodes and a central control node is completely restored, after 338 minutes, PMC arrives and repairs the fault line 8-9, and closes the lines and the lines 12-22 so that the loads 8-12 and loads 19 and 20 are powered back, and after 438 minutes, the service personnel arrives and repairs the line 9-15, closing the line and the lines 18-33 to ensure that the load nodes 13-18 recover power supply, repairing the line 6-26 distribution point at the 583 th minute, recovering the power supply of the load nodes 26 and 27 at the moment, completely recovering the power supply of all the loads, rearranging the dispatching aiming at the shortest time for repairing the residual fault elements by a primary maintainer and a secondary maintainer, wherein the dispatching path is as shown in figure 7, and when the dispatching path reaches 1374min, the fault elements are completely repaired, and the power distribution information network recovers normal operation.
Example two
The second embodiment of the disclosure introduces an elastic power distribution network post-disaster recovery system based on information physical collaborative optimization.
As shown in fig. 8, the system for recovering an elastic power distribution network after disaster based on information physical collaborative optimization includes:
an acquisition module configured to acquire post-disaster physical information of an elastic distribution network;
the post-disaster recovery module is configured to realize post-disaster recovery of the power distribution network according to the acquired information and a preset recovery model;
the recovery model comprises a first-stage recovery sub-model and a second-stage recovery sub-model, wherein the first-stage recovery sub-model performs post-disaster network reconstruction of the power distribution network by taking the minimum loss load after a fault occurs as a target function and taking scheduling path constraint of maintenance personnel, time unified constraint of parallel maintenance personnel and network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after all the loads recover power supply as a target function and takes the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions to repair the fault elements of the reconstructed power distribution network after the disaster and perform the cooperative recovery of the power distribution network after the disaster.
The detailed steps are the same as those of the elastic distribution network post-disaster recovery method based on information physical collaborative optimization provided in the first embodiment, and are not described herein again.
EXAMPLE III
The third embodiment of the disclosure provides a computer-readable storage medium.
A computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the resilient power distribution network after-disaster recovery method based on cyber-physical collaborative optimization according to the first embodiment of the present disclosure.
The detailed steps are the same as those of the elastic distribution network post-disaster recovery method based on information physical collaborative optimization provided in the first embodiment, and are not described herein again.
Example four
The fourth embodiment of the disclosure provides an electronic device.
An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the resilient power distribution network after-disaster recovery method based on cyber-physical collaborative optimization according to an embodiment of the present disclosure.
The detailed steps are the same as those of the elastic distribution network post-disaster recovery method based on information physical collaborative optimization provided in the first embodiment, and are not described herein again.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1. An elastic power distribution network post-disaster recovery method based on information physical collaborative optimization is characterized by comprising the following steps:
acquiring post-disaster physical information of the elastic power distribution network;
according to the acquired information and a preset recovery model, the post-disaster recovery of the power distribution network is realized;
the recovery model comprises a first-stage recovery sub-model and a second-stage recovery sub-model, wherein the first-stage recovery sub-model performs post-disaster network reconstruction of the power distribution network by taking the minimum loss load after a fault occurs as a target function and taking scheduling path constraint of maintenance personnel, time unified constraint of parallel maintenance personnel and network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after all the loads recover power supply as a target function and takes the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions to repair the fault elements of the reconstructed power distribution network after the disaster and perform the cooperative recovery of the power distribution network after the disaster.
2. The elastic distribution network post-disaster recovery method based on information physical collaborative optimization as claimed in claim 1, characterized in that in the first stage of recovery, the primary repair is matched with the secondary repair and the network reconstruction, and the primary maintenance personnel and the secondary maintenance personnel are scheduled and a reasonable network reconstruction strategy is implemented; in the second stage of recovery process, the primary maintenance personnel and the secondary maintenance personnel are rescheduled and arranged, and the primary maintenance personnel and the secondary maintenance personnel are matched to quickly recover the fault element.
3. The elastic distribution network post-disaster recovery method based on cyber-physical collaborative optimization as claimed in claim 1, wherein the recovery model is a distribution network recovery model with variable time step triggered based on a repair event, and specifically comprises:
each time, information element repair activity and physical element repair activity are regarded as a repair event to trigger a network reconstruction judgment, when two or more repair events are completed at the same time, the repair events can be regarded as different repair times with the time interval of 0 to trigger the network reconstruction, and the load loss after the disaster is reduced through the coordinated operation of the first repair, the second repair and the network reconstruction.
4. The elastic distribution network post-disaster recovery method based on information physical collaborative optimization as claimed in claim 1, wherein the scheduling path constraint of the maintenance personnel is as follows: and (3) restraining the repair paths, repair sequences and repair resources of the primary and secondary maintainers, establishing a complete path model for repairing the fault element by a plurality of maintainers in parallel, and setting a path decision variable corresponding to a path at the repair time to be 0 corresponding to the maintainers not completing the repair activity if the maintainers complete the repair activity at different fault times.
5. The elastic distribution network post-disaster recovery method based on information physical collaborative optimization as claimed in claim 1, wherein the time unification constraint of the parallel maintenance personnel is as follows: according to the relation between the repair time and the repair path, firstly, the repair time sequences corresponding to the individual repair activities of different maintenance teams are established, the repair time of other repair teams not completing the repair activities at a certain repair moment is set to be 0 through the repair activity flag bit, then the repair activities of all teams are unified, the sequences corresponding to the real repair activities and arranged according to the sequence of the occurrence of the events are obtained, and the subsequent triggering of network reconstruction judgment is facilitated.
6. The elastic distribution network post-disaster recovery method based on information physics collaborative optimization as claimed in claim 1, wherein the objective function of the first stage recovery submodel is min Σk∑i(1-δi,k)*p_loadi*(Δtk) Wherein, deltai,tkFor the load recovery coefficient, p _ load, of load node i after the kth repair activity is completed and reconstructediIs the active power of the load node i, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
7. The elastic distribution network post-disaster recovery method based on information physics collaborative optimization as claimed in claim 1, wherein the objective function of the second-stage recovery submodel is min ΣkΔtkWherein, Δ tkThe time interval between the k-th network reconstruction and the last network reconstruction.
8. The utility model provides an elasticity distribution network recovery system after disaster based on information physics collaborative optimization which characterized in that includes:
an acquisition module configured to acquire post-disaster physical information of an elastic distribution network;
the post-disaster recovery module is configured to realize post-disaster recovery of the power distribution network according to the acquired information and a preset recovery model;
the recovery model comprises a first-stage recovery sub-model and a second-stage recovery sub-model, wherein the first-stage recovery sub-model performs post-disaster network reconstruction of the power distribution network by taking the minimum loss load after a fault occurs as a target function and taking scheduling path constraint of maintenance personnel, time unified constraint of parallel maintenance personnel and network reconstruction constraint under the influence of information physical coupling as constraint conditions; and the second-stage recovery sub-model takes the minimum time cost for repairing the residual fault elements after all the loads recover power supply as a target function and takes the scheduling path constraint of maintenance personnel and the time unified constraint of parallel maintenance personnel as constraint conditions to repair the fault elements of the reconstructed power distribution network after the disaster and perform the cooperative recovery of the power distribution network after the disaster.
9. A computer-readable storage medium, on which a program is stored, wherein the program, when executed by a processor, implements the steps in the resilient power distribution network after-disaster recovery method based on cyber-physical co-optimization according to any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the resilient distribution network after-disaster recovery method based on cyber-physical collaborative optimization according to any one of claims 1 to 7.
CN202210073634.4A 2022-01-21 2022-01-21 Elastic power distribution network post-disaster recovery method and system based on information physical collaborative optimization Pending CN114389263A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115313487A (en) * 2022-08-22 2022-11-08 西安交通大学 Mobile hydrogen energy microgrid scheduling method considering maintenance flow
CN115809836A (en) * 2023-02-09 2023-03-17 华南理工大学 Distribution network toughness planning method considering distributed energy storage emergency power supply capacity
CN117934210A (en) * 2024-03-25 2024-04-26 国网山西省电力公司营销服务中心 Power distribution network system restoring force evaluation method based on construction of multiple evaluation indexes

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115313487A (en) * 2022-08-22 2022-11-08 西安交通大学 Mobile hydrogen energy microgrid scheduling method considering maintenance flow
CN115809836A (en) * 2023-02-09 2023-03-17 华南理工大学 Distribution network toughness planning method considering distributed energy storage emergency power supply capacity
CN117934210A (en) * 2024-03-25 2024-04-26 国网山西省电力公司营销服务中心 Power distribution network system restoring force evaluation method based on construction of multiple evaluation indexes
CN117934210B (en) * 2024-03-25 2024-05-24 国网山西省电力公司营销服务中心 Power distribution network system restoring force evaluation method based on construction of multiple evaluation indexes

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