Sequential optimization method and device for multi-team collaborative repair of power distribution network under multi-source factor

Abstract

The invention discloses a sequential optimization method and device for multi-team collaborative repair of a power distribution network under a multi-source factor. The method comprises the following steps: firstly, constructing a line fault probability model based on a first external source and internal source set factor, and constructing a line maintenance time model based on a second external source and internal source set factor; in the prevention stage, a reinforcement strategy is generated according to the fault probability model, and a preventive island is constructed; in the degradation stage, a fault line set is determined according to a fault probability model and a threshold; in the recovery stage, the two models are combined, and a multi-team composite VRP scheduling decision model is constructed by using a variable time scale strategy; and finally, integrating information of each stage, constructing a multi-stage toughness improvement model by taking minimization of active weighted loss as a target, and solving to obtain an optimal pre-disaster reinforcement and post-disaster recovery strategy. According to the invention, the adaptation and recovery capability of the power distribution network to extreme weather can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network resilience improvement, and particularly relates to a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method and device. BACKGROUND

[0002] With the continuous change of global climate, the frequent and intensified extreme weather disasters have brought unprecedented challenges to the safe and stable power supply of power systems. In recent years, with the construction of smart grids and the vigorous development of renewable energy, more and more uncertainties from various aspects have had a wide and significant impact on the operation and planning of power systems. In previous studies, the uncertainty in power systems was considered to be caused by inaccurate prediction of external factors and unrelated to decision-making. However, in other cases, there is an inherent link between uncertainty and decision-making, and this type of uncertainty is classified as decision-dependent uncertainty, also known as endogenous uncertainty.

[0003] Extreme natural disasters, such as hurricanes, earthquakes, or ice storms, often cause damage to a large number of components in the power distribution network, leading to high-order failures. Since power cannot be restored until these damages are repaired, reasonable pre-disaster reinforcement prevention and post-disaster failure repair strategies are essential to maximize the adaptability and recovery ability of the power distribution network to extreme weather. A single repair team cannot quickly respond to all failure repair challenges, so multiple repair teams need to work together to handle high-order complex failure repair tasks. After a failure occurs in the power distribution network, in order to restore power as soon as possible and ensure efficient use of energy, the influence of exogenous and endogenous uncertainty needs to be considered, and the power resource, switch resource, and human resource of multiple repair teams need to be coordinated to improve the rapid response and self-recovery ability of the power distribution network. Considering the severity of extreme weather disasters and the limited nature of emergency resources, developing efficient pre-disaster proactive defense reinforcement and post-disaster multi-repair team flexible response strategies is of great significance to improving the resilience of the power distribution network.

[0004] Therefore, how to invent a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method has become a problem to be solved, which can effectively improve the adaptability and recovery ability of the power distribution network to extreme weather. SUMMARY

[0005] To this end, the present application provides a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method and device, which can effectively improve the adaptability and recovery ability of the power distribution network to extreme weather.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method, comprising:

[0007] constructing a line fault probability model based on the first exogenous setting factor and the first endogenous setting factor;

[0008] constructing a line repair time model based on the second exogenous setting factor and the second endogenous setting factor;

[0009] In the prevention stage before the arrival of extreme weather, a reinforcement strategy is generated through probability analysis by the line fault probability model; and the line is reinforced according to the reinforcement strategy and a preventive island is formed;

[0010] In the degradation stage after the occurrence of extreme weather disasters, a fault line set is constructed according to the line fault probability model and a fault probability threshold;

[0011] In the recovery stage after the occurrence of extreme weather disasters, a multi-maintenance team composite VRP scheduling decision model is constructed through a variable time scale strategy based on the line fault probability model and the line repair time model;

[0012] A power distribution network multi-stage resilience improvement model is constructed by integrating the reinforcement strategy in the prevention stage, the fault line set in the degradation stage and the multi-maintenance team composite VRP scheduling decision model in the recovery stage, with the objective function being to minimize the active weighted loss in the degradation stage and the recovery stage;

[0013] By solving the power distribution network multi-stage resilience improvement model, an optimal pre-disaster reinforcement strategy and post-disaster recovery strategy are obtained.

[0014] As the preferred solution of the multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method, the first exogenous setting factor is typhoon wind speed; the first endogenous setting factor is the influence of reinforcement decisions on line fault rates in the power distribution network; the second exogenous setting factor includes environmental conditions and available repair resources; and the second endogenous setting factor includes the influence of reinforcement decisions and maintenance decisions on line repair times in the power distribution network.

[0015] As the preferred solution of the multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method, the expression of the line fault probability model is:

[0016]

[0017]

[0018] wherein, v max,ij,s is the maximum wind speed experienced by the power distribution line (i, j) during the typhoon experience process of scenario s; is the fault probability of tower k in line (i, j) of scenario s; is the median of the cumulative distribution function of the fault probability; is the standard deviation; is the failure probability of line l in scenario s line (i, j); is the line failure rate directly caused by wind load of typhoon; is the probability of trees near the line being blown down or broken by the wind; is the average probability of causing overhead line failure when tree lodging occurs near the line; is the failure probability of scenario s line (i, j) under reinforcement measure h; is the number of towers of line (i, j); is the number of lines of line (i, j); is whether reinforcement measure h is adopted for line (i, j); is the tower set of line (i, j); is the line set of line (i, j).

[0019] As the preferred scheme of the multi-source factor-based multi-team collaborative repair sequential optimization method for distribution networks, the expression of the line repair time model is:

[0020]

[0021] In the formula, ω t is the repair time weight factor; is the repair start time; ω h is the reinforcement weight factor; is the basic repair time required for line n; is whether reinforcement measure h is adopted for line n; t n,s is the time required for repair of line n in scenario s.

[0022] As the preferred scheme of the multi-source factor-based multi-team collaborative repair sequential optimization method for distribution networks, the expression of the objective function for minimizing the active weighted loss in the degradation phase and the recovery phase is:

[0023]

[0024] In the formula, S is the set of failure scenarios; B is the set of nodes; L is the set of line numbers corresponding; p s is the probability of scenario s; is the load shedding cost of node j; T D is the system degradation phase duration; Δτ n,s is the duration from the end of the repair of the previous line in the system recovery phase to the end of the repair of line n in scenario s; and are the load shedding sizes of node j in the time period of repair line n in the degradation and recovery phases in scenario s.

[0025] As the preferred solution of the multi-source factor under the distribution network multi-team collaborative repair sequential optimization method, the constraints of line reinforcement in the prevention stage before the arrival of extreme weather include reinforcement constraints, prevention stage operation constraints and radial topology constraints;

[0026] The expression of the reinforcement constraint is:

[0027]

[0028] In the formula, Ω L is a set of lines represented by two end nodes of the line; S is a set of fault scenarios; h is the reinforcement measure number index, h = 0 represents no reinforcement; H is a set of reinforcement measures; is whether the line (i, j) adopts the measure h; is the reinforcement cost of the line (i, j) adopting the measure h; C H is the total reinforcement budget of the system;

[0029] The expression of the prevention stage operation constraint is:

[0030]

[0031] In the formula, P L,j and Q L,j are the active and reactive load demands of node j, respectively; and are the active and reactive power of the line (j, u) in the prevention stage, respectively; and are the active and reactive power of the line (i, j) in the prevention stage, respectively; and are the active and reactive power of the line (i, j) in the prevention stage, respectively; and are the active and reactive power of the line (i, j) in the prevention stage, respectively; ij is the resistance of the line (i, j); x ij is the reactance of the line (i, j); are the voltages of nodes i and j in the prevention stage, respectively, and U0 is the reference voltage; is the lower limit of the voltage of node j; is the upper limit of the voltage of node j; and are the upper limits of the active and reactive power of DG, respectively; and are the upper limits of the active and reactive power of the substation, respectively; is the on-off state of the line (i, j) in the prevention stage, which is 1 if the line is in the closed state, otherwise it is 0; is the child set of node j; is the parent set of node j; M is a large positive integer, taking 1000; is the upper limit of the capacity of line (i, j); is whether DG is connected to node j, 1 if connected, otherwise 0; is whether the substation is connected to node j, 1 if connected, otherwise 0.

[0032] The expression of the radial topology constraint is:

[0033]

[0034] In the formula, is whether node j is the root node in the preventive phase; is the virtual flow on virtual edge ij in the preventive phase; is the virtual flow on virtual edge ji in the preventive phase; N B is the total number of nodes; Ω RCS is the branch set equipped with a remotely controllable switch.

[0035] As the preferred scheme of the multi-source factor-based multi-team collaborative repair sequential optimization method of the distribution network, in the degradation phase after the occurrence of extreme weather disasters, the constraints of the fault line set include: line fault state constraint, fault area constraint and degradation phase operation constraint;

[0036] The expression of the line fault state constraint is:

[0037]

[0038] In the formula, p th is the fault probability threshold; is the fault state of line (i, j) in the degradation phase under scenario s, if line (i, j) fails, is 1, otherwise 0; after the occurrence of typhoon disasters, if the line failure probability exceeds the threshold p th under the condition of adopting measure h, the line is considered to fail After the occurrence of typhoon disasters, if the line failure probability does not exceed the threshold p th under the condition of adopting measure h, the line can maintain normal operation M is a large positive integer; ε is a small positive number;

[0039] The expression of the fault area constraint is:

[0040]

[0041] In the formula, are binary variables, if nodes i and j are connected to the fault after the scenario s accident and degradation process, Conversely, all are 0;

[0042] The expression for the operational constraints of the degradation phase is:

[0043]

[0044] In the formula, and This reduces the active and reactive loads of node j during the degradation phase.

[0045] As a preferred scheme for the sequential optimization method of multi-team collaborative repair of distribution networks under multiple factors, the constraints of the multi-maintenance team composite VRP scheduling decision model in the recovery phase after extreme weather disasters include: maintenance team scheduling VRP constraints, fault repair time sequencing VRP constraints, fault state constraints in the recovery phase, fault area constraints in the recovery phase, radial topology constraints in the recovery phase, and safe operation constraints in the recovery phase.

[0046] The expression for the VRP constraint for the maintenance team scheduling is:

[0047]

[0048]

[0049] In the formula, m and n are the line numbers; Ω A ={0,1,2,…,N L N L +1} represents the set of locations reachable by the maintenance team, where {0} represents the departure warehouse, and {N} represents the location of the team. L +1} represents arrival at the warehouse; k is the index of the maintenance team number; K is the set of maintenance teams; d k,m,n,s Let d be a marker indicating the location of the maintenance team. If the k-th maintenance team moves from line m to line n, then d k,m,n,s =1, otherwise 0; β m,n,s Let β be a 0-1 variable. If the same maintenance team repairs lines m and n sequentially, then β m,n,s =1, otherwise 0;

[0050] The expression for the fault repair time sequencing VRP constraint is:

[0051]

[0052] In the formula, Ω E ={0,1,2,…,N L Let {} be the set of reachable locations in the Virtual Repair Response (VRP) sequence, where {0} represents the start time of the recovery phase; define two N values. L +1-dimensional time set set elements is the start time of the recovery phase; is the start time and the end time of the repair action of the nth line; T m,n is the transfer time of the repair team between m and n; γ m,n,s is the fault repair sequence flag, if the system is repaired in the order of m first and n second, then γ m,n,s = 1, otherwise 0; Δτ n,s is the time interval between the end time of the repair action of the line n and the time when the previous fault is repaired, that is, the duration of the time period n;

[0053] The expression of the fault state constraint of the recovery phase is:

[0054]

[0055] In the formula, is the line fault state flag, if the line m, (i, j) is damaged in the time period n, then it is 1; if the line m, (i, j) is not damaged or the fault has been repaired in the time period n, then it is 0;

[0056] The expression of the fault area constraint of the recovery phase is:

[0057]

[0058] In the formula, is whether the node i, j is in the fault area in the time period n of the recovery phase of the scene s; is whether the line (i, j) is closed in the time period n of the recovery phase of the scene s;

[0059] The expression of the radial topology constraint of the recovery phase is:

[0060]

[0061] In the formula, is whether the node j is the root node in the time period n of the recovery phase of the scene s; π(j) and denote the parent and child node set of the node j; is the virtual flow on the virtual edge ij in the time period n of the recovery phase of the scene s; is the virtual flow on the virtual edge ji in the time period n of the recovery phase of the scene s; N B is the total number of nodes; Ω RCS is the branch set equipped with a remotely controllable switch;

[0062] The expression of the safe operation constraint of the recovery phase is:

[0063]

[0064] In the formula, PL,j and Q L,j Pj and Qj represent the active power demand of node j, respectively; and Pij and Qij represent the active and reactive power of line (i, j) in the recovery phase time period n of scenario s, respectively; and Pj and Qj represent the active and reactive load shedding of node j in the recovery phase time period n of scenario s, respectively; and Pj and Qj represent the active and reactive power output of power supply at node j in the recovery phase time period n of scenario s, respectively; and Pj and Qj represent the active and reactive power output of substation at node j in the recovery phase time period n of scenario s, respectively; r ij Rij represents the resistance of line (i, j); x ij Xij represents the reactance of line (i, j); Uj and Uj represent the voltage of node i and j in the recovery phase time period n of scenario s, respectively; U0 represents the reference voltage; Uj represents the lower limit of voltage of node j; Uj represents the upper limit of voltage of node j; and Pj and Qj represent the upper limit of active and reactive power output of DG, respectively; and Pj and Qj represent the upper limit of active and reactive power output of substation, respectively; and Pij and Qij represent the active and reactive power of line (j, u) in the recovery phase time period n of scenario s, respectively.

[0065] The application also provides a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization device, based on the above multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method, comprising:

[0066] A line fault probability model construction module is configured to construct a line fault probability model based on the first external source setting factor and the first internal source setting factor;

[0067] A line repair time model construction module is configured to construct a line repair time model based on the second external source setting factor and the second internal source setting factor;

[0068] A prevention phase processing module is configured to, in a prevention phase before the arrival of extreme weather, perform probability analysis through the line fault probability model to generate a reinforcement strategy; and reinforce the line according to the reinforcement strategy and form a preventive island;

[0069] A degradation phase processing module is configured to, in a degradation phase after the occurrence of extreme weather disasters, construct a fault line set according to the line fault probability model and a fault probability threshold;

[0070] The recovery phase processing module is configured to, in a recovery phase after the occurrence of the extreme weather disaster, construct a multi-maintenance team composite VRP scheduling decision model by a variable time scale strategy based on the line fault probability model and the line maintenance time model.

[0071] The distribution network multi-stage resilience improvement model construction module is configured to construct a distribution network multi-stage resilience improvement model by taking minimization of active weighted loss in the degradation phase and the recovery phase as an objective function, integrating the reinforcement strategy in the prevention phase, the set of fault lines in the degradation phase, and the multi-maintenance team composite VRP scheduling decision model in the recovery phase.

[0072] The distribution network multi-stage resilience improvement model solution module is configured to obtain optimal pre-disaster reinforcement strategies and post-disaster recovery strategies by solving the distribution network multi-stage resilience improvement model.

[0073] As the preferred scheme of the distribution network multi-team collaborative repair sequential optimization device under multiple source factors, in the line fault probability model construction module, the first external source setting factor is a typhoon wind speed; and the first internal source setting factor is an influence of reinforcement decisions on line fault rates in the distribution network.

[0074] In the line maintenance time model construction module, the second external source setting factor includes environmental conditions and available repair resources; and the second internal source setting factor includes influences of reinforcement decisions and maintenance decisions on line maintenance times in the distribution network.

[0075] As the preferred scheme of the distribution network multi-team collaborative repair sequential optimization device under multiple source factors, in the line fault probability model construction module, an expression of the line fault probability model is as follows:

[0076]

[0077]

[0078] In the formula, v max,ij,s is a maximum wind speed experienced by a distribution line (i, j) during a typhoon experience process of a scenario s; is a fault probability of a tower k in a line (i, j) of a scenario s; is a median of a cumulative distribution function of the fault probability; is a standard deviation; is a fault probability of a conductor l in a line (i, j) of a scenario s; is a line fault rate directly caused by wind load of the typhoon; is a probability of trees near the conductor being blown down or broken by the wind; is an average probability of causing overhead conductor failure when trees near the conductor are downed; is the failure probability of the line (i, j) in the scenario s under the reinforcement measure h; is the number of towers of the line (i, j); is the number of conductors of the line (i, j); is whether the line (i, j) adopts the reinforcement measure h; is the tower set of the line (i, j); is the conductor set of the line (i, j).

[0079] As the preferred scheme of the power distribution network multi-team collaborative repair sequential optimization device under multiple source factors, in the line repair time model construction module, the expression of the line repair time model is:

[0080]

[0081] In the formula, ω t is the repair time weight factor; is the repair start time; ω h is the reinforcement weight factor; is the basic repair time required by the line n; is whether the line n adopts the reinforcement measure h; t n,s is the time required by the repair line n in the scenario s.

[0082] As the preferred scheme of the power distribution network multi-team collaborative repair sequential optimization device under multiple source factors, in the power distribution network multi-stage resilience improvement model construction module, the expression of the objective function taking minimizing the active weighted loss of the degradation stage and the recovery stage as the objective function is:

[0083]

[0084] In the formula, S is a set of failure scenarios; B is a set of nodes; L is a set of line corresponding numbers; p s is the probability of the scenario s; is the load shedding cost of the node j; T D is the system degradation stage duration; Δτ n,s is the duration from the end of the line repair in the previous line repair stage to the end of the line n repair in the recovery stage of the scenario s system; and is the load shedding size of the node j in the time period of the repair line n in the degradation and recovery stages under the scenario s.

[0085] As the preferred scheme of the power distribution network multi-team collaborative repair sequential optimization device under multiple source factors, in the prevention stage processing module, the constraints of reinforcing the line in the prevention stage before the extreme weather arrives include: reinforcement constraints, prevention stage operation constraints and radial topology constraints;

[0086] The expression of the reinforcement constraint is:

[0087]

[0088] where Ω L is the set of lines represented by the two end nodes of the line; S is the set of fault scenarios; h is the index of reinforcement measures, h=0 represents no reinforcement; H is the set of reinforcement measures; is whether the line (i,j) adopts the measure h; is the reinforcement cost of the line (i,j) adopting the measure h; C H is the total reinforcement budget of the system;

[0089] The expression of the preventive stage operation constraint is:

[0090]

[0091] where P L,j and Q L,j are the active and reactive load demand of node j, respectively; and are the active and reactive power of the line (j,u) in the preventive stage, respectively; and are the active and reactive power of the line (i,j) in the preventive stage, respectively; and are the active and reactive output of the power source at node j in the preventive stage, respectively; and are the active and reactive output of the substation at node j in the preventive stage, respectively; r ij is the resistance of the line (i,j); x ij is the reactance of the line (i,j); are the voltages of nodes i and j in the preventive stage, respectively, and U0 is the base voltage; is the lower limit of the voltage of node j; is the upper limit of the voltage of node j; and are the upper limits of the active and reactive output of the DG, respectively; and are the upper limits of the active and reactive output of the substation, respectively; is the on-off state of the line (i,j) in the preventive stage, which is 1 if the line is in the closed state, otherwise it is 0; is the set of child nodes of node j; π(j) is the set of parent nodes of node j; M is a large positive integer, taking 1000; is the upper limit of the capacity of the line (i,j); is whether the DG is connected to node j, which is 1 if connected, otherwise it is 0; is whether the substation is connected to node j, which is 1 if connected, otherwise it is 0.

[0092] The expression of the radial topology constraint is:

[0093]

[0094] wherein, is whether node j is a root node in the preventive phase; is the virtual flow on virtual edge ij in the preventive phase; is the virtual flow on virtual edge ji in the preventive phase; B is the total number of nodes; Ω RCS is the branch set equipped with remotely controllable switches.

[0095] As the preferred scheme of the power distribution network multi-team collaborative repair sequential optimization device under the multi-source factor, in the degradation phase after the occurrence of extreme weather disasters, the constraint of constructing the fault line set in the degradation phase processing module includes: line fault state constraint, fault area constraint and degradation phase operation constraint;

[0096] The expression of the line fault state constraint is:

[0097]

[0098] wherein, p th is the fault probability threshold value; is the fault state of line (i, j) in the degradation phase under scenario s, if line (i, j) fails, then it is 1; after the occurrence of typhoon disasters, if the line failure probability exceeds the threshold value p th in the case of adopting measure h, the line is considered to have failed After the occurrence of typhoon disasters, if the line failure probability does not exceed the threshold value p th in the case of adopting measure h, the line can maintain normal operation M is a large positive integer; ε is a small positive number;

[0099] The expression of the fault area constraint is:

[0100]

[0101] wherein, are both binary variables, if nodes i and j are connected with faults after scenario s accident and degradation process, then they are both 0;

[0102] The expression of the degradation phase operation constraint is:

[0103]

[0104] wherein, and This reduces the active and reactive loads of node j during the degradation phase.

[0105] As a preferred scheme for the sequential optimization device for multi-team collaborative repair of distribution networks under multiple factors, in the recovery phase processing module, during the recovery phase after extreme weather disasters, the constraints for constructing the multi-maintenance team composite VRP scheduling decision model include: maintenance team scheduling VRP constraints, fault repair time sequencing VRP constraints, recovery phase fault state constraints, recovery phase fault area constraints, recovery phase radial topology constraints, and recovery phase safe operation constraints.

[0106] The expression for the VRP constraint for the maintenance team scheduling is:

[0107]

[0108]

[0109] In the formula, m and n are the line numbers; Ω A ={0,1,2,…,N L N L +1} represents the set of locations reachable by the maintenance team, where {0} represents the departure warehouse, and {N} represents the location of the team. L +1} represents arrival at the warehouse; k is the index of the maintenance team number; K is the set of maintenance teams; d k,m,n,s Let d be a marker indicating the location of the maintenance team. If the k-th maintenance team moves from line m to line n, then d k,m,n,s =1, otherwise 0; β m,n,s Let β be a 0-1 variable. If the same maintenance team repairs lines m and n sequentially, then β m,n,s =1, otherwise 0;

[0110] The expression for the fault repair time sequencing VRP constraint is:

[0111]

[0112] In the formula, Ω E ={0,1,2,…,N L Let {} be the set of reachable locations in the Virtual Repair Response (VRP) sequence, where {0} represents the start time of the recovery phase; define two N values. L +1-dimensional time set set elements This marks the start of the recovery phase. T represents the start and end times of the repair operation on the nth line; m,n γ represents the transfer time of the maintenance team between m and n; m,n,sis a flag for the fault repair sequence, if the system is repaired in the order of m before n, then γ m,n,s = 1, otherwise 0; Δτ n,s is the time interval between the time when the repair of line n is completed and the time when the previous fault is repaired, i.e., the duration of time period n;

[0113] The expression of the fault state constraint in the recovery phase is:

[0114]

[0115] wherein, is a flag for the fault state of line m, (i, j), if line m, (i, j) is damaged in time period n, then it is 1; if line m, (i, j) is not damaged or the fault has been repaired in time period n, then it is 0;

[0116] The expression of the fault area constraint in the recovery phase is:

[0117]

[0118] wherein, is whether node i, j is in the fault area in time period n in the recovery phase of scenario s; is whether line (i, j) is closed in time period n in the recovery phase of scenario s;

[0119] The expression of the radial topology constraint in the recovery phase is:

[0120]

[0121] wherein, is whether node j is the root node in time period n in the recovery phase of scenario s; π(j) and represent the parent and child node sets of node j; is the virtual flow on virtual edge ij in time period n in the recovery phase of scenario s; is the virtual flow on virtual edge ji in time period n in the recovery phase of scenario s; N B is the total number of nodes; Ω RCS is the branch set equipped with a remotely controllable switch;

[0122] The expression of the safe operation constraint in the recovery phase is:

[0123]

[0124]

[0125] wherein, P L,j and Q L,j respectively represent the active load demand of node j; and are the active and reactive power of line (i, j) in scenario s restoration phase time period n, respectively; and are the active and reactive load curtailment of node j in scenario s restoration phase time period n, respectively; and are the active and reactive power supply of node j in scenario s restoration phase time period n, respectively; and are the active and reactive substation power of node j in scenario s restoration phase time period n, respectively; r ij is the resistance of line (i, j); x ij is the reactance of line (i, j); are the voltages of nodes i, j in scenario s restoration phase time period n, respectively; U0 is the base voltage; is the lower voltage limit of node j; is the upper voltage limit of node j; and are the upper limits of DG active and reactive power, respectively; and are the upper limits of substation active and reactive power, respectively; and are the active and reactive power of line (j, u) in scenario s restoration phase time period n, respectively.

[0126] The application has the following advantages: the application constructs a line fault probability model based on a first exogenous setting factor and a first endogenous setting factor; constructs a line maintenance time model based on a second exogenous setting factor and a second endogenous setting factor; in a prevention stage before the arrival of extreme weather, probability analysis is performed through the line fault probability model to generate a reinforcement strategy; according to the reinforcement strategy, the line is reinforced to form a preventive island; in a degradation stage after the occurrence of extreme weather disasters, a fault line set is constructed according to the line fault probability model and a fault probability threshold; in a recovery stage after the occurrence of extreme weather disasters, a multi-maintenance team composite VRP scheduling decision model is constructed through a variable time scale strategy based on the line fault probability model and the line maintenance time model; a power distribution network multi-stage resilience improvement model is constructed with the objective function of minimizing the active weighted loss in the degradation stage and the recovery stage, integrating the reinforcement strategy in the prevention stage, the fault line set in the degradation stage and the multi-maintenance team composite VRP scheduling decision model in the recovery stage; the optimal pre-disaster reinforcement strategy and post-disaster recovery strategy are obtained by solving the power distribution network multi-stage resilience improvement model. The application can give flexible and reliable active defense and flexible response strategies in response to extreme weather disasters. The application considers the influence of endogenous uncertainty on fault probability and maintenance time, and solves the repair sequencing problem of multi-maintenance team coupling through a maintenance team scheduling VRP model and a maintenance time sequencing VRP model, and uses a variable time step idea for modeling, thereby improving the efficiency and accuracy of solving the fault repair strategy, and effectively enhancing the resilience level of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0127] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be derived from the provided drawings without creative labor.

[0128] The structures, proportions, sizes, etc. shown in the specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not have technical significance to limit the conditions that the application can be implemented, so any modification of the structure, change of the proportion relationship or adjustment of the size, which does not affect the effects that the application can produce and the purposes that the application can achieve, should still fall within the scope of the technical content disclosed by the application.

[0129] Figure 1 A flowchart of the multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method provided in embodiment 1 of the application;

[0130] Figure 2 Fig. 1 is a schematic diagram of the initial state of the IEEE 123 node system and a disaster scenario in one possible embodiment provided in Embodiment 1 of the present application;

[0131] Figure 3 Fig. 4 is a schematic diagram of the maintenance time weight factor in one possible embodiment provided in Embodiment 1 of the present application;

[0132] Figure 4 Fig. 5 is a schematic diagram of the system state in the degradation stage of the IEEE 123 node system in one possible embodiment provided in Embodiment 1 of the present application;

[0133] Figure 5 Fig. 6 is a schematic diagram of the load recovery of the IEEE 123 node system in one possible embodiment provided in Embodiment 1 of the present application;

[0134] Figure 6 Fig. 7 is a schematic diagram of the architecture of the multi-source factor-based power distribution network multi-team collaborative repair sequential optimization device provided in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0135] The embodiments of the present application will be described in detail by specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0136] Embodiment 1

[0137] Referring to Figure 1 Embodiment 1 of the present application provides a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization method, including the following steps:

[0138] S1, constructing a line fault probability model based on a first external source setting factor and a first internal source setting factor;

[0139] S2, constructing a line maintenance time model based on a second external source setting factor and a second internal source setting factor;

[0140] S3, in the prevention stage before the arrival of extreme weather, performing probability analysis through the line fault probability model to generate a reinforcement strategy; according to the reinforcement strategy, reinforcing the line and forming a preventive island;

[0141] S4, in the degradation stage after the occurrence of extreme weather disasters, constructing a fault line set according to the line fault probability model and a fault probability threshold;

[0142] S5, in the recovery stage after the occurrence of extreme weather disasters, based on the line failure probability model and the line repair time model, a multi-maintenance team composite VRP scheduling decision model is constructed through a variable time scale strategy;

[0143] S6, taking the minimization of the active weighted loss in the degradation stage and the recovery stage as the objective function, the reinforcement strategy in the prevention stage, the failure line set in the degradation stage and the multi-maintenance team composite VRP scheduling decision model in the recovery stage are integrated to construct a multi-stage resilience improvement model of the distribution network;

[0144] S7, by solving the multi-stage resilience improvement model of the distribution network, the optimal pre-disaster reinforcement strategy and post-disaster recovery strategy are obtained.

[0145] In this embodiment, in step S1, a line failure probability model is constructed based on a first exogenous setting factor and a first endogenous setting factor;

[0146] The first exogenous setting factor is the wind speed of a typhoon, and the first endogenous setting factor is the influence of the reinforcement decision on the line failure rate in the distribution network.

[0147] Specifically, considering the exogenous uncertainty factors such as the wind speed of a typhoon and the endogenous uncertainty factors such as the influence of the reinforcement decision on the line failure rate in the distribution network, a line failure probability model is constructed.

[0148] The expression of the line failure probability model is:

[0149]

[0150] In the formula, v max,ij,s is the maximum wind speed experienced by the distribution line (i, j) during the typhoon experience process of scenario s; is the failure probability of tower k in line (i, j) in scenario s; is the median of the cumulative distribution function of the failure probability; is the standard deviation; is the failure probability of conductor l in line (i, j) in scenario s; is the line failure rate directly caused by the wind load of the typhoon; is the probability of trees near the conductor being blown down or broken by the wind; is the average probability of causing overhead conductor failure when trees near the conductor are downed; is the failure probability of line (i, j) in scenario s under reinforcement measure h; is the number of towers of line (i, j); is the number of conductors of line (i, j); is whether reinforcement measure h is adopted for line (i, j); a set of towers for line (i, j); a set of conductors for line (i, j).

[0151] In this embodiment, in step S2, a line repair time model is constructed based on second exogenous setting factors and second endogenous setting factors;

[0152] The second exogenous setting factors include environmental conditions and available repair resources, and the second endogenous setting factors include the influence of reinforcement decisions and repair decisions on line repair time in the power distribution network.

[0153] Specifically, considering exogenous uncertainty factors such as environmental conditions and available repair resources, and endogenous uncertainty factors such as the influence of reinforcement decisions and repair decisions on line repair time in the power distribution network, a line repair time model is constructed;

[0154] The expression of the line repair time model is:

[0155]

[0156] In the formula, ω t is a repair time weight factor; is a repair start time; ω h is a reinforcement weight factor; is the required basic repair time of line n; is whether reinforcement measure h is adopted for line n; t n,s is the required time of repair line n in scenario s.

[0157] In this embodiment, in step S3, in the prevention stage before the arrival of extreme weather, a reinforcement strategy is generated by performing probability analysis through the line failure probability model; and the line is reinforced according to the reinforcement strategy to form a preventive island;

[0158] Specifically, in the prevention stage before the arrival of extreme weather, the line is reinforced and a preventive island is formed based on the analysis of the line failure probability model;

[0159] The constraints for reinforcing the line in the prevention stage before the arrival of extreme weather include reinforcement constraints, prevention stage operation constraints, and radial topology constraints.

[0160] The expression of the reinforcement constraints is:

[0161]

[0162] In the formula, Ω L is a set of lines represented by two end nodes of the line; S is a set of failure scenarios; h is a reinforcement measure number index, h=0 represents no reinforcement; and H is a set of reinforcement measures. for line (i, j) whether to adopt measure h; for reinforcement cost of line (i, j) adopting measure h; C H for total reinforcement budget of system;

[0163] According to the pre-disaster constraint, any line can adopt only one reinforcement measure or no reinforcement, and the pre-disaster reinforcement cost cannot exceed the budget.

[0164] In this embodiment, the expression of the prevention stage operation constraint is:

[0165]

[0166]

[0167] In the formula, P L,j and Q L,j are active and reactive load demands of node j, respectively; and are active and reactive power of line (j, u) in the prevention stage, respectively; and are active and reactive power of line (i, j) in the prevention stage, respectively; and are active and reactive power of line (i, j) in the prevention stage, respectively; and are active and reactive power of line (i, j) in the prevention stage, respectively; ij is resistance of line (i, j); ij is reactance of line (i, j); are voltages of nodes i and j in the prevention stage, respectively, and U0 is a reference voltage; is a lower limit of voltage of node j; is an upper limit of voltage of node j; and are upper limits of active and reactive power of DG, respectively; and are upper limits of active and reactive power of substation, respectively; is an on-off state of line (i, j) in the prevention stage, and is 1 if the line is in a closed state, otherwise is 0; is a child node set of node j; π(j) is a parent node set of node j; and M is a large integer, taking 1000; is an upper limit of capacity of line (i, j); is whether DG is connected to node j, and is 1 if connected, otherwise is 0; is whether substation is connected to node j, and is 1 if connected, otherwise is 0.

[0168] The expression for the radial topological constraint is:

[0169]

[0170] In the formula, To prevent whether stage node j is the root node; To prevent virtual traffic on the virtual edge ij during the phase; Virtual traffic on the virtual edge during the prevention phase; N B Ω represents the total number of nodes. RCS A collection of branches equipped with remotely controllable switches.

[0171] The aforementioned operational constraints and radial topological constraints during the prevention phase can ensure the safe and stable operation of the power distribution system.

[0172] In this embodiment, in step S4, during the degradation stage after an extreme weather disaster, a set of faulty lines is constructed based on the line fault probability model and the fault probability threshold.

[0173] Specifically, during the degradation phase following an extreme weather disaster, the constraints for constructing the set of faulty lines include: line fault state constraints, fault area constraints, and degradation phase operation constraints.

[0174] The expression for the line fault state constraint is:

[0175]

[0176] In the formula, p th This is the failure probability threshold; Let (i,j) be the fault state of line (i,j) in the degradation stage under scenario s. If line (i,j) fails, then Conversely, it is 0; after a typhoon disaster, if the probability of line failure exceeds the threshold p under the condition of taking measure h. th If so, it is considered that the line has a fault. After a typhoon disaster, if the probability of line failure does not exceed the threshold p under the condition that measure h is adopted. th The line can maintain normal operation. M is a large positive integer; ε is a small positive number;

[0177] Based on the line fault state constraints, it is possible to obtain the set of faulty lines in the distribution network after a typhoon disaster scenario s occurs, taking into account both external uncertainties (typhoon wind speed) and internal uncertainties (reinforcement decisions).

[0178] The expression for the fault region constraint is:

[0179]

[0180] In the formula, Both are binary variables. If nodes i and j in the post-accident degradation process of scenario s are connected to the fault, then Conversely, all are 0;

[0181] Fault region constraints can accurately describe the influence range of the faulty line, ensuring the reliability of the final solution results.

[0182] The expression for the operational constraints of the degradation phase is:

[0183]

[0184] In the formula, and This reduces the active and reactive loads of node j during the degradation phase.

[0185] Degradation phase operational constraints can limit nodes in the fault zone from losing power supply capability, i.e., losing all load.

[0186] In this embodiment, in step S5, during the recovery phase after an extreme weather disaster, a multi-maintenance team composite VRP scheduling decision model is constructed based on the line fault probability model and the line maintenance time model through a variable time scale strategy.

[0187] Specifically, during the recovery phase following an extreme weather disaster, the constraints for constructing the multi-maintenance team composite VRP scheduling decision model include: maintenance team scheduling VRP constraints, fault repair time sequencing VRP constraints, recovery phase fault state constraints, recovery phase fault area constraints, recovery phase radial topology constraints, and recovery phase safe operation constraints.

[0188] The expression for the VRP constraint for the maintenance team scheduling is:

[0189]

[0190]

[0191] In the formula, m and n are the line numbers; Ω A ={0,1,2,…,N L N L +1} represents the set of locations reachable by the maintenance team, where {0} represents the departure warehouse, and {N} represents the location of the team. L +1} represents arrival at the warehouse; k is the index of the maintenance team number; K is the set of maintenance teams; d k,m,n,s Let d be a marker indicating the location of the maintenance team. If the k-th maintenance team moves from line m to line n, then d k,m,n,s =1, otherwise 0; β m,n,s Let β be a 0-1 variable. If the same maintenance team repairs lines m and n sequentially, then β m,n,s= 1, otherwise 0;

[0192] The scheduling strategy of traversing all teams and all fault lines can be obtained according to the maintenance team scheduling VRP constraint.

[0193] The expression of the fault repair time ordering VRP constraint is:

[0194]

[0195] In the formula, Ω E ={0, 1, 2, …, N L} is a set of virtual maintenance order VRP reachable positions, wherein {0} represents the start time of the recovery phase; two N L +1-dimensional time sets The set elements are the start time of the recovery phase; are the start time and completion time of the n-th line repair action; T m,n represents the transfer time of the maintenance team between m and n; γ m,n,s is a fault repair order flag, and if the system as a whole is repaired in the order of m first and n second, then γ m,n,s = 1, otherwise 0; Δτ n,s is the time interval between the completion time of the repair action of line n and the repair of the previous fault, i.e. the duration of time period n;

[0196] According to the fault repair time ordering VRP constraint, the coupling relationship between the repair times of fault lines can be obtained, i.e. if the maintenance team repairs fault lines m and n in turn (β m,n,s = 1), then the repair of fault line n lags behind the repair of fault line m, and the time difference between the repair end times is including the time of the maintenance team transferring from fault m to fault n and the time required for repairing fault n; the maintenance order is regarded as a virtual VRP, and the transfer of the maintenance order is constrained by the completion time of the repair action.

[0197] The expression of the recovery phase fault state constraint is:

[0198]

[0199] In the formula, is a line fault state flag, and if the line m, (i, j) is damaged in the time period n, then it is 1; if the line m, (i, j) is not damaged or has been repaired in the time period n, then it is 0;

[0200] The recovery phase fault state constraint effectively describes the influence of the maintenance team scheduling decision on the line fault state.

[0201] The expression of the recovery phase fault area constraint is:

[0202]

[0203] In the formula, is whether nodes i and j are in the fault area in the recovery phase time period n of scenario s; is whether the line (i, j) is closed in the recovery phase time period n of scenario s;

[0204] The recovery phase fault area constraint can accurately describe the influence range of the fault line and ensure the reliability of the final solution result.

[0205] The expression of the recovery phase radial topology constraint is:

[0206]

[0207]

[0208] In the formula, is whether node j is a root node in the recovery phase time period n of scenario s; and indicates the parent and child node set of node j; is the virtual flow on the virtual edge ij in the recovery phase time period n of scenario s; is the virtual flow on the virtual edge ji in the recovery phase time period n of scenario s; N B is the total number of nodes; Ω RCS is the branch set equipped with a remotely controllable switch;

[0209] The expression of the recovery phase safe operation constraint is:

[0210]

[0211] In the formula, P L,j and Q L,j respectively indicate the active load demand of node j; and respectively are the active and reactive power of the line (i, j) in the recovery phase time period n of scenario s; and respectively are the active and reactive load reduction of node j in the recovery phase time period n of scenario s; and respectively are the active and reactive power output of the power source at node j in the recovery phase time period n of scenario s; and respectively are the active and reactive power output of the substation at node j in the recovery phase time period n of scenario s; r ij is the resistance of the line (i, j); x ijis the voltage of node j in the recovery phase time period n of scenario s; U0 is the reference voltage; is the voltage of node j in the recovery phase time period n of scenario s; U0 is the reference voltage; is the lower limit of the voltage of node j; is the upper limit of the voltage of node j; and are the upper limits of the active and reactive power of DG, respectively; and are the upper limits of the active and reactive power of the substation, respectively; and are the active and reactive power of line (j, u) in the recovery phase time period n of scenario s.

[0212] The recovery phase radial topology constraint and the recovery phase safe operation constraint can ensure safe and stable operation of the power distribution system.

[0213] In this embodiment, in step S6, the reinforcement strategy in the prevention phase, the set of fault lines in the degradation phase, and the multi-maintenance team composite VRP scheduling decision model in the recovery phase are integrated to construct a multi-stage resilience improvement model of the power distribution network, with minimization of the weighted active loss in the degradation phase and the recovery phase as the objective function.

[0214] Specifically, the expression of the objective function with minimization of the weighted active loss in the degradation phase and the recovery phase as the objective function is as follows:

[0215]

[0216] In the formula, S is a set of fault scenarios; B is a set of nodes; L is a set of line numbers; p s is the probability of scenario s; is the load shedding cost of node j; T D is the duration of the system degradation phase; Δτ n,s is the duration from the end of the maintenance of the previous line to the end of the maintenance of line n in the recovery phase of the system of scenario s; and is the load shedding size of node j in the time period of maintenance line n in the degradation and recovery phases of scenario s.

[0217] In this embodiment, in step S7, the optimal pre-disaster reinforcement strategy and post-disaster recovery strategy are obtained by solving the multi-stage resilience improvement model of the power distribution network.

[0218] Specifically, the multi-stage resilience improvement model of the power distribution network is solved by a solver such as Cplex or Gurobi, to obtain the optimal pre-disaster reinforcement strategy and post-disaster recovery strategy.

[0219] In one possible embodiment, an IEEE 123 node system verification example is provided as follows:

[0220] In the IEEE 123 node system, node 1 is connected to a substation, and node 36, node 88 and node 122 are equipped with controllable distributed power supply, and the predicted three typhoon scene paths are as shown in Figure 2

[0221] The optimal pre-disaster reinforcement strategy obtained by the present application is shown in Table 1:

[0222]

[0223]

[0224] Table 1 Optimal pre-disaster reinforcement strategy

[0225] Taking scenario one as an example, it is assumed that the degradation stage starts at 10 o'clock in the morning and lasts for two hours; the recovery stage starts at 12 o'clock noon.

[0226] The maintenance time weight factor diagram of the present application is shown in Figure 3 The system state of the IEEE 123 node system scenario one degradation stage is shown in Figure 4

[0227] The fault line and its maintenance start time and maintenance time weight factor of scenario one obtained by the present application are shown in Table 2:

[0228] Faulty line Time of repair start Time of repair weight factor Strengthening weight factor Line 43-44 16:28 1 1 Line 26-29 20:25 1.1 1 Line 64-65 17:13 1 1 Line 98-122 20:17 1.1 1 Line 101-116 00:10 (next morning) 1.2 0.8

[0229] Table 2 Fault line and its maintenance start time and required maintenance time of scenario one

[0230] According to Table 2, it is illustrated that the present application considers the influence of endogenous uncertainty in line fault state and line maintenance time.

[0231] The optimal post-disaster fault repair strategy of scenario one obtained by the present application is shown in Table 3:

[0232]

[0233] Table 3 Fault line and its maintenance start time and required maintenance time of scenario one

[0234] According to Table 3, it is illustrated that the present application realizes the collaborative scheduling of multi-maintenance team considering endogenous uncertainty.

[0235] The system load recovery state of the IEEE 123 node system scenario one is shown in Figure 5 ​​As shown, it is illustrated that the application realizes the overall repair sequential optimization function of multi-failure and multi-maintenance team cooperation considering endogenous uncertainty.

[0236] In summary, before the arrival of typhoons, earthquakes, ice storms and other extreme weather, the optimal line reinforcement scheme is formulated for the distribution network line in combination with the line failure probability model (considering exogenous uncertainty such as typhoon wind speed and endogenous uncertainty such as reinforcement decision). By selecting appropriate reinforcement measures such as vegetation management and tower reinforcement, the key lines are reinforced, and the preventive island is constructed to improve the ability of the distribution network to resist disasters and reduce the number of line failures that may be caused by disasters.

[0237] After the occurrence of extreme weather disasters, based on the pre-disaster reinforcement decision and the line failure probability model, in combination with the failure probability threshold, the lines that have failed in the distribution network are accurately identified, and the fault line set is constructed. At the same time, by means of fault area constraint, the influence range of the fault is determined, and the area and load loss caused by the fault are determined to provide clear fault objects and influence boundaries for subsequent repair work.

[0238] After the fault line set is determined, based on the line repair time model (considering exogenous uncertainty such as environmental conditions and repair resources, and endogenous uncertainty such as reinforcement decision and repair decision), a multi-maintenance team compound VRP scheduling decision model is established by using the variable time scale method. Through the model, the scheduling route of multiple maintenance teams (maintenance team scheduling VRP) and the fault repair sequence (fault repair time sequencing VRP) are reasonably planned, the human resources and repair resources are coordinated, the fault lines are repaired at the fastest speed, the power supply of the distribution network is restored, and the active weighted loss in the degradation and recovery stages is minimized.

[0239] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of the embodiments can also be applied in a distributed scenario, and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present disclosure, and the multiple devices can interact with each other to complete the method.

[0240] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those described above and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0241] Embodiment 2

[0242] Referring to Figure 6 Embodiment 2 also provides a multi-source factor-based power distribution network multi-team collaborative repair sequential optimization device, which comprises:

[0243] A line fault probability model construction module 001 is configured to construct a line fault probability model based on first external source setting factors and first internal source setting factors;

[0244] A line repair time model construction module 002 is configured to construct a line repair time model based on second external source setting factors and second internal source setting factors;

[0245] A prevention stage processing module 003 is configured to, in a prevention stage before the arrival of extreme weather, perform probability analysis through the line fault probability model to generate a reinforcement strategy, and reinforce the line according to the reinforcement strategy to form a preventive island;

[0246] A degradation stage processing module 004 is configured to, in a degradation stage after the occurrence of extreme weather disasters, construct a fault line set according to the line fault probability model and a fault probability threshold;

[0247] A recovery stage processing module 005 is configured to, in a recovery stage after the occurrence of extreme weather disasters, construct a multi-repair team composite VRP scheduling decision model through a variable time scale strategy based on the line fault probability model and the line repair time model;

[0248] A power distribution network multi-stage resilience improvement model construction module 006 is configured to, with the minimization of active weighted losses in the degradation stage and the recovery stage as an objective function, integrate the reinforcement strategy in the prevention stage, the fault line set in the degradation stage, and the multi-repair team composite VRP scheduling decision model in the recovery stage to construct a power distribution network multi-stage resilience improvement model;

[0249] A power distribution network multi-stage resilience improvement model solving module 007 is configured to obtain optimal pre-disaster reinforcement strategies and post-disaster recovery strategies by solving the power distribution network multi-stage resilience improvement model.

[0250] In the line fault probability model construction module 001, the first external source setting factor is a typhoon wind speed, and the first internal source setting factor is the influence of reinforcement decisions on the line fault rate in the power distribution network.

[0251] In the line repair time model construction module 002, the second external source setting factors include environmental conditions and available repair resources, and the second internal source setting factors include the influence of reinforcement decisions and repair decisions on the line repair time in the power distribution network.

[0252] In this embodiment, in the line fault probability model construction module 001, the expression of the line fault probability model is:

[0253]

[0254] In the formula, v max,ij,s is the maximum wind speed experienced by the distribution line (i, j) during the typhoon experience process of the scene s; is the fault probability of the tower k in the line (i, j) of the scene s; is the median of the cumulative distribution function of the fault probability; is the standard deviation; is the fault probability of the conductor l in the line (i, j) of the scene s; is the line failure rate directly caused by the wind load of the typhoon; is the probability of trees near the conductor being blown down or broken; is the average probability of causing overhead conductor failure when tree lodging occurs near the conductor; is the fault probability of the line (i, j) of the scene s under the reinforcement measure h; is the number of towers of the line (i, j); is the number of conductors of the line (i, j); is whether the line (i, j) adopts the reinforcement measure h; is the tower set of the line (i, j); is the conductor set of the line (i, j).

[0255] In this embodiment, in the line repair time model construction module 002, the expression of the line repair time model is:

[0256]

[0257] In the formula, ω t is a repair time weight factor; is the repair start time; ω h is a reinforcement weight factor; is the basic repair time required for the line n; is whether the line n adopts the reinforcement measure h; t n,s is the time required for repairing the line n of the scene s.

[0258] In this embodiment, in the distribution network multi-stage resilience improvement model construction module 006, the expression of the objective function for minimizing the active weighted loss of the degradation stage and the recovery stage is:

[0259]

[0260] where S is a set of failure scenarios; B is a set of nodes; L is a set of line pair indexes; p s is a probability of scenario s; is the load shedding cost of node j; T D is the duration of system degradation phase; Δτ n,s is the duration from the end of the repair of line n to the end of the repair of line n before the system recovery phase of scenario s; and is the load shedding size of node j in the time period of repair line n during the degradation and recovery phase of scenario s.

[0261] In the embodiment, in the prevention phase before the extreme weather, the constraints of reinforcing the line in the prevention phase processing module 003 include reinforcement constraints, prevention phase operation constraints and radial topology constraints;

[0262] The expression of the reinforcement constraint is:

[0263]

[0264] where Ω L is a set of lines represented by two end nodes; S is a set of failure scenarios; h is a reinforcement measure index, h = 0 represents no reinforcement; H is a set of reinforcement measures; is whether the line (i, j) adopts measure h; is the reinforcement cost of the line (i, j) adopting measure h; C H is the total reinforcement budget of the system;

[0265] The expression of the prevention phase operation constraint is:

[0266]

[0267] where P L,j and Q L,j are the active and reactive load demands of node j, respectively; and are the active and reactive power of line (j, u) in the prevention phase, respectively; and are the active and reactive power output of the power supply at node j in the prevention phase, respectively; and are the active and reactive power output of the substation at node j in the prevention phase, respectively; r ij is the resistance of line (i, j); x ij is the reactance of line (i, j); are the voltages of nodes i and j in the prevention phase, respectively, and U0 is the reference voltage; is the lower limit of the voltage of node j; is the upper limit of the voltage of node j; and These are the maximum active and reactive power outputs of DG, respectively. and These are the upper limits of active and reactive power output of the substation, respectively. To prevent the opening and closing state of the line (i,j) in the stage, the value is 1 if the line is closed, and 0 otherwise. Let be the set of child nodes of node j; π(j) be the set of parent nodes of node j; M is a large positive integer, taking the value 1000; This represents the maximum capacity of line (i,j); This indicates whether DG is connected to node j; if connected, it is 1, otherwise it is 0. This indicates whether the substation is connected to node j; if connected, it is 1, otherwise it is 0.

[0268] The expression for the radial topological constraint is:

[0269]

[0270] In the formula, To prevent whether stage node j is the root node; To prevent virtual traffic on the virtual edge ij during the phase; Virtual traffic on the virtual edge during the prevention phase; N B Ω represents the total number of nodes. RCS A collection of branches equipped with remotely controllable switches.

[0271] In this embodiment, in the degradation stage processing module 004, during the degradation stage after an extreme weather disaster, the constraints for constructing the set of faulty lines include: line fault state constraints, fault area constraints, and degradation stage operation constraints.

[0272] The expression for the line fault state constraint is:

[0273]

[0274] In the formula, p th This is the failure probability threshold; Let (i,j) be the fault state of line (i,j) in the degradation stage under scenario s. If line (i,j) fails, then Conversely, it is 0; after a typhoon disaster, if the probability of line failure exceeds the threshold p under the condition of taking measure h. th If so, it is considered that the line has a fault. After a typhoon disaster, if the probability of line failure does not exceed the threshold p under the condition that measure h is adopted. th The line can maintain normal operation. M is a large positive integer; ε is a small positive number;

[0275] The expression for the fault region constraint is:

[0276]

[0277] In the formula, Both are binary variables. If nodes i and j in the post-accident degradation process of scenario s are connected to the fault, then Conversely, all are 0;

[0278] The expression for the operational constraints of the degradation phase is:

[0279]

[0280] In the formula, and This reduces the active and reactive loads of node j during the degradation phase.

[0281] In this embodiment, in the recovery phase processing module 005, during the recovery phase after an extreme weather disaster, the constraints for constructing the multi-maintenance team composite VRP scheduling decision model include: maintenance team scheduling VRP constraints, fault repair time sequencing VRP constraints, recovery phase fault state constraints, recovery phase fault area constraints, recovery phase radial topology constraints, and recovery phase safe operation constraints.

[0282] The expression for the VRP constraint for the maintenance team scheduling is:

[0283]

[0284]

[0285] In the formula, m and n are the line numbers; Ω A ={0,1,2,…,N L N L +1} represents the set of locations reachable by the maintenance team, where {0} represents the departure warehouse, and {N} represents the location of the team. L +1} represents arrival at the warehouse; k is the index of the maintenance team number; K is the set of maintenance teams; d k,m,n,s Let d be a marker indicating the location of the maintenance team. If the k-th maintenance team moves from line m to line n, then d k,m,n,s =1, otherwise 0; β m,n,s Let β be a 0-1 variable. If the same maintenance team repairs lines m and n sequentially, then β m,n,s =1, otherwise 0;

[0286] The expression for the fault repair time sequencing VRP constraint is:

[0287]

[0288] where Ω E = {0, 1, 2, …, N L} is the set of reachable positions of the virtual repair sequence VRP, where {0} represents the beginning time of the recovery phase; two N L +1 dimensional time sets are defined is the beginning time of the recovery phase; is the beginning time and completion time of the nth line repair action; T m,n represents the transfer time of the repair team between m and n; γ m,n,s is the fault repair sequence flag bit, if the system as a whole is repaired in the order of m first and n second, then γ m,n,s = 1, otherwise 0; Δτ n,s is the time interval between the completion time of the line n repair action and the repair of the previous fault, i.e. the duration of the time period n;

[0289] The expression of the recovery phase fault state constraint is:

[0290]

[0291] wherein, is the line fault state flag bit, if the line m, (i, j) is damaged in the time period n, then it is 1; if the line m, (i, j) is not damaged or the fault has been repaired in the time period n, then it is 0;

[0292] The expression of the recovery phase fault area constraint is:

[0293]

[0294] wherein, is whether the node i, j is in the fault area in the time period n of the recovery phase of the scene s; is whether the line (i, j) is closed in the time period n of the recovery phase of the scene s;

[0295] The expression of the recovery phase radial topology constraint is:

[0296]

[0297] wherein, is whether the node j is the root node in the time period n of the recovery phase of the scene s; π(j) and represent the parent and child node sets of the node j; is the virtual flow on the virtual edge ij in the time period n of the recovery phase of the scene s; is the virtual flow on the virtual edge ji in the time period n of the recovery phase of the scene s; N B is the total number of nodes; Ω RCSa branch set equipped with remotely controllable switches;

[0298] The expression of the safe operation constraint in the recovery phase is:

[0299]

[0300] wherein P L,j and Q L,j respectively represent the active load demand of node j; and respectively represent the active power and the reactive power of line (i, j) in the time period n of the recovery phase of scenario s; and respectively represent the active load reduction and the reactive load reduction of node j in the time period n of the recovery phase of scenario s; and respectively represent the active power and the reactive power of the power supply at node j in the time period n of the recovery phase of scenario s; and respectively represent the active power and the reactive power of the substation at node j in the time period n of the recovery phase of scenario s; r ij is the resistance of line (i, j); x ij is the reactance of line (i, j); respectively represent the voltage of node i and node j in the time period n of the recovery phase of scenario s; U0 is the reference voltage; is the lower limit of the voltage of node j; is the upper limit of the voltage of node j; and respectively represent the upper limit of the active power and the upper limit of the reactive power of DG; and respectively represent the upper limit of the active power and the upper limit of the reactive power of the substation; and respectively represent the active power and the reactive power of line (j, u) in the time period n of the recovery phase of scenario s.

[0301] It should be noted that the information interaction and execution process between the modules of the system described above are based on the same concept as the method embodiments in Embodiment 1 of the present application, and the technical effects brought by them are the same as those of the method embodiments of the present application. For specific content, refer to the description in the foregoing method embodiments of the present application.

[0302] Embodiment 3

[0303] Embodiment 3 of the present application provides a non-transitory computer readable storage medium, wherein the computer readable storage medium stores program codes of a multi-source factor based power distribution network multi-team collaborative repair sequential optimization method, and the program codes include instructions for executing the multi-source factor based power distribution network multi-team collaborative repair sequential optimization method of Embodiment 1 or any possible implementation manner thereof.

[0304] The computer-readable storage medium can be any available media or a set of one or more of the available media that is accessible by a computer, a server, a data center, etc. data storage device integrated. The available media can be a magnetic medium, (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0305] Embodiment 4

[0306] Embodiment 4 of the present application provides an electronic device, comprising: a memory and a processor;

[0307] The processor and the memory complete the communication between each other through the bus; the memory stores program instructions executable by the processor, and the processor calling the program instructions can execute the multi-source factor power distribution network multi-team collaborative repair sequential optimization method of embodiment 1 or any possible implementation manner thereof.

[0308] Specifically, the processor can be implemented by hardware or software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented by software, the processor can be a general-purpose processor, which realizes by reading software codes stored in the memory. The memory can be integrated in the processor or exist independently outside the processor.

[0309] In the above embodiments, all or part of them can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part of them can be realized in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable systems. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode.

[0310] It should be apparent to those skilled in the art that the modules or steps of the application described above can be implemented with a general purpose computing system, which can be centralized on a single computing system or distributed over a network of multiple computing systems, and optionally implemented with program code executable by a computing system, which can be stored in a storage system and executed by a computing system, and in some cases, the steps shown or described can be performed in a different order than shown or described, or made into individual integrated circuit modules or multiple modules or steps made into a single integrated circuit module. Thus, the application is not limited to any particular combination of hardware and software.

[0311] While the application has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various modifications or changes can be made therein without departing from the spirit and scope of the application. Accordingly, it is intended that all such modifications and changes be included within the scope of the application as claimed.

Claims

1. A multi-source factor power distribution network multi-team collaborative repair sequential optimization method, characterized in that, The method comprises the following steps: constructing a line fault probability model based on a first exogenous setting factor and a first endogenous setting factor; constructing a line repair time model based on a second exogenous setting factor and a second endogenous setting factor; in a prevention stage before the arrival of extreme weather, performing probability analysis through the line fault probability model to generate a reinforcement strategy; reinforcing the line according to the reinforcement strategy and forming a preventive island; in a degradation stage after the occurrence of extreme weather disasters, constructing a fault line set according to the line fault probability model and a fault probability threshold; in a recovery stage after the occurrence of extreme weather disasters, constructing a multi-maintenance team composite VRP scheduling decision model through a variable time scale strategy based on the line fault probability model and the line repair time model; taking the minimization of active power weighted loss in the degradation stage and the recovery stage as an objective function, integrating the reinforcement strategy in the prevention stage, the fault line set in the degradation stage and the multi-maintenance team composite VRP scheduling decision model in the recovery stage to construct a multi-stage resilience improvement model of a power distribution network; obtaining an optimal pre-disaster reinforcement strategy and post-disaster recovery strategy by solving the multi-stage resilience improvement model of the power distribution network.

2. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 1, characterized in that, The first exogenous setting factor is a typhoon wind speed; The first endogenous setting factor is the influence of reinforcement decisions on the line fault rate in the power distribution network; The second exogenous setting factor includes environmental conditions and available repair resources; and the second endogenous setting factor includes the influence of reinforcement decisions and maintenance decisions on the line repair time in the power distribution network.

3. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 2, characterized in that, The expression of the line fault probability model is: where v max,ij,s is the maximum wind speed experienced by the power distribution line (i, j) during the typhoon experienced by the scenario s; is the failure probability of the tower k in the line (i, j) in the scenario s; is the median of the cumulative distribution function of the failure probability; is the standard deviation; is the failure probability of the conductor l in the line (i, j) in the scenario s; is the line failure rate directly caused by the wind load of the typhoon; is the probability of the trees near the conductor being blown down or broken by the wind; is the average probability of causing the overhead conductor to fail when tree lodging occurs near the conductor; is the failure probability of the line (i, j) in the scenario s under the reinforcement measure h; is the number of towers of the line (i, j); is the number of conductors of the line (i, j); is whether the reinforcement measure h is adopted for the line (i, j); is the tower set of the line (i, j); is the conductor set of the line (i, j).

4. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 3, characterized in that, The expression of the line repair time model is: where ω t is the repair time weight factor; is the repair start time; ω h is the reinforcement weight factor; is the base repair time required for line n; is whether reinforcement measure h is used for line n; t n,s is the time required for repairing line n in scenario s.

5. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 4, characterized in that, The expression of the objective function taking the minimization of active power weighted loss in the degradation stage and the recovery stage as an objective function is: In the formula, S is a fault scenario set; B is a node set; L is a line corresponding number set; p s is the probability of scenario s; is the load shedding cost of node j; T D is the system degradation phase duration; Δτ n,s is the duration of the recovery phase for scenario s, from the end of the previous line repair to the end of the repair of line n; and is the amount of load shed from node j during the repair of line n in the degradation and recovery phase for scenario s.

6. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 5, characterized in that, In the prevention stage before the arrival of extreme weather, the constraints for reinforcing the line include reinforcement constraints, prevention stage operation constraints and radial topology constraints; The expression of the reinforcement constraints is: where Ω L is the set of lines represented by the two end nodes of the line; S is the set of failure scenarios; h is the reinforcement measure number index, h = 0 represents no reinforcement; H is the set of reinforcement measures; is whether the line (i, j) adopts measure h; is the reinforcement cost of the line (i, j) adopting measure h; C H is the total reinforcement budget of the system; The expression of the prevention stage operation constraints is: where P L,j and Q L,j are the active and reactive load demand of node j, respectively; and are the active and reactive power of line (j, u) in the preventive phase, respectively; and are the active and reactive power of line (i, j) in the preventive phase, respectively; and are the active and reactive power output of the power supply at node j in the preventive phase, respectively; and are the active and reactive power output of the substation at node j in the preventive phase, respectively; r ij is the resistance of line (i, j); x ij is the reactance of line (i, j); are the voltage of node i and j in the preventive phase, respectively, and U0 is the base voltage; is the lower limit of voltage of node j; is the upper limit of voltage of node j; and are the upper limit of active and reactive power output of DG, respectively; and are the upper limit of active and reactive power output of substation, respectively; is the on-off state of line (i, j) in the preventive phase, which is 1 if the line is in the closed state, otherwise 0; is the child node set of node j, and π(j) is the parent node set of node j; M is a large positive integer, which is 1000; is the upper limit of capacity of line (i, j); is whether DG is connected to node j, which is 1 if connected, otherwise 0; is whether the substation is connected to node j, which is 1 if connected, otherwise 0. The expression of the radial topology constraints is: wherein is the virtual traffic on the preventive phase edge ij; is the virtual traffic on the preventive phase edge ij; is the virtual traffic on the preventive phase edge ij; B is the total number of nodes; RCS is the set of branches equipped with remotely controllable switches.

7. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 6, characterized in that, In the degradation stage after the occurrence of extreme weather disasters, the constraints for constructing the fault line set include line fault state constraints, fault area constraints and degradation stage operation constraints; The expression of the line fault state constraints is: wherein p th is a failure probability threshold value; is the failure state of the line (i, j) in the degradation stage under the scenario s, if the line (i, j) fails, then is 1, otherwise 0; after the occurrence of the typhoon disaster, if the line failure probability exceeds the threshold value p th under the adoption of the measure h, it is considered that the line fails After the occurrence of the typhoon disaster, if the line failure probability does not exceed the threshold value p th under the adoption of the measure h, the line can maintain normal operation M is a large positive integer; and ε is a small positive number. The expression of the fault area constraints is: wherein are binary variables, equal to 1 if the scenario s degrades after the accident at the process node i, j is connected to the failure, and are equal to 0 otherwise. The expression of the degradation stage operation constraints is: wherein and Pj and Qj are the active and reactive power load curtailment of the degeneration stage node j.

8. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization method according to claim 7, characterized in that, In the recovery stage after the occurrence of extreme weather disasters, the constraints for constructing the multi-maintenance team composite VRP scheduling decision model include maintenance team scheduling VRP constraints, fault repair time sequencing VRP constraints, recovery stage fault state constraints, recovery stage fault area constraints, recovery stage radial topology constraints and recovery stage safe operation constraints; The expression of the maintenance team scheduling VRP constraints is: where m, n are line numbers; Ω A = {0, 1, 2, …, N L L +1} is the set of reachable locations for the repair team, where {0} represents the departure warehouse and {N L +1} represents the arrival warehouse; k is the index of the repair team number; K is the set of repair teams; d k,m,n,s is the repair team position movement flag, if the kth repair team moves from line m to line n, then d k,m,n,s = 1, otherwise 0; β m,n,s is a 0-1 variable, if the same repair team repairs lines m, n in turn, then β m,n,s = 1, otherwise 0;​ The expression of the fault repair time sequencing VRP constraints is: In the formula, Ω E ={0,1,2,…,N L Let {} be the set of reachable locations in the Virtual Repair Response (VRP) sequence, where {0} represents the start time of the recovery phase; define two N values. L +1-dimensional time set set elements This marks the start of the recovery phase. T represents the start and end times of the repair operation on the nth line; m,n γ represents the transfer time of the maintenance team between m and n; m,n,s This is a fault repair sequence flag. If the entire system is repaired sequentially in the order of m then n, then γ m,n,s =1, otherwise 0; Δτ n,s The time interval between the completion of line n repair action and the repair of the previous fault, i.e., the duration of time interval n; The expression of the recovery stage fault state constraints is: In the formula, is a line fault state flag bit, and is 1 if the line m, (i, j) is damaged in the time period n; and is 0 if the line m, (i, j) is not damaged or the damage has been repaired in the time period n. The expression of the recovery stage fault area constraints is: wherein is whether nodes i, j are in the fault zone during the recovery phase time period n for scenario s; is whether the line (i,j) is closed during the recovery phase time period n for scenario s. The expression of the recovery stage radial topology constraints is: wherein, is whether node j is a root node in the recovery phase time period n for scenario s; π(j) and denote the parent and child node set of node j; is the virtual traffic on virtual edge ij in the recovery phase time period n for scenario s; is the virtual traffic on virtual edge ji in the recovery phase time period n for scenario s; N B is the total number of nodes; Ω RCS is the set of branches equipped with remotely controllable switches; The expression of the recovery stage safe operation constraints is: where P L,j and Q L,j represent the active load demand of node j, respectively; and are the active and reactive power of line (i, j) in scenario s restoration phase time period n, respectively; and are the active and reactive load curtailment of node j in scenario s restoration phase time period n, respectively; and are the active and reactive power output of source at node j in scenario s restoration phase time period n, respectively; and are the active and reactive power output of substation at node j in scenario s restoration phase time period n, respectively; r ij is the resistance of line (i, j); x ij is the reactance of line (i, j); are the voltages of nodes i, j in scenario s restoration phase time period n, respectively; U0is the base voltage; is the lower voltage limit of node j; is the upper voltage limit of node j; and are the upper limits of DG active and reactive power output, respectively; and are the upper limits of substation active and reactive power output, respectively; and are the active and reactive power of line (j, u) in scenario s restoration phase time period n, respectively.

9. The device for sequential optimization of multi-team collaborative restoration of a distribution network under multiple sources, adopts the method for sequential optimization of multi-team collaborative restoration of a distribution network under multiple sources according to any one of claims 1-8, characterized in that, The method comprises the following steps: The line fault probability model construction module is configured to construct a line fault probability model based on the first exogenous setting factor and the first endogenous setting factor; The line repair time model construction module is configured to construct a line repair time model based on the second exogenous setting factor and the second endogenous setting factor; The prevention stage processing module is configured to, in a prevention stage before the extreme weather arrives, perform probability analysis through the line fault probability model to generate a reinforcement strategy, and reinforce the line according to the reinforcement strategy to form a preventive island; The degradation stage processing module is configured to, in a degradation stage after the extreme weather disaster occurs, construct a fault line set according to the line fault probability model and a fault probability threshold; The recovery stage processing module is configured to, in a recovery stage after the extreme weather disaster occurs, construct a multi-repair team composite VRP scheduling decision model through a variable time scale strategy based on the line fault probability model and the line repair time model; The distribution network multi-stage resilience improvement model construction module is configured to, with minimization of active weighted loss in the degradation stage and the recovery stage as an objective function, integrate the reinforcement strategy in the prevention stage, the fault line set in the degradation stage, and the multi-repair team composite VRP scheduling decision model in the recovery stage to construct a distribution network multi-stage resilience improvement model; The distribution network multi-stage resilience improvement model solution module is configured to obtain optimal pre-disaster reinforcement strategies and post-disaster recovery strategies by solving the distribution network multi-stage resilience improvement model.

10. The multi-source factor-based distribution network multi-team collaborative repair sequential optimization device according to claim 9, characterized in that, In the line fault probability model construction module, the first exogenous setting factor is a typhoon wind speed, and the first endogenous setting factor is an influence of reinforcement decisions on line fault rates in the distribution network. In the line repair time model construction module, the second exogenous setting factor includes environmental conditions and available repair resources, and the second endogenous setting factor includes influences of reinforcement decisions and repair decisions on line repair times in the distribution network.

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