CN113312761B - Method and system for improving toughness of power distribution network - Google Patents

Method and system for improving toughness of power distribution network Download PDF

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CN113312761B
CN113312761B CN202110536801.XA CN202110536801A CN113312761B CN 113312761 B CN113312761 B CN 113312761B CN 202110536801 A CN202110536801 A CN 202110536801A CN 113312761 B CN113312761 B CN 113312761B
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component
power
time
distribution network
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CN113312761A (en
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吕泉成
姜明凯
梁智健
黄雅莉
邓明
陈旭东
王彦伦
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a method and a system for improving toughness of a power distribution network, which are used for evaluating importance of power distribution network components in typhoon weather, establishing an element fault rate model under storm action, sampling the state of a wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sequencing the importance of the components; acquiring fault conditions when disasters occur, and preferentially recovering elements with high importance according to the determined sequence to realize the improvement of the robustness of the power distribution network; the method comprises the steps of pre-deploying a mobile emergency power generation vehicle before a disaster and performing real-time scheduling after the disaster to form a plurality of micro-grids, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem by the objective function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster by solving the corresponding problem, and completing the recovery and the promotion of the power distribution network. The method and the device realize the improvement of the robustness of the power distribution network; the fault recovery time of the power distribution network is shortened, and the rapidity of power distribution network recovery is realized.

Description

Method and system for improving toughness of power distribution network
Technical Field
The invention belongs to the technical field of safety planning operation of power systems, and particularly relates to a method and a system for improving toughness of a power distribution network.
Background
In recent years, frequent natural disasters bring serious challenges to a power system, the defect that the power system is insufficient in coping capability against extreme disasters is highlighted, especially typhoons have wide influence on the power system, strong breaking power, and the power grid equipment has a plurality of tripping times and concentrated time during typhoons logging in the border, and is mostly in permanent faults, the fault equipment is difficult to recover in time, and as a key link for directly serving users, the power distribution network is easy to have severe power failure accidents under typhoons. In 2017, typhoons and "Tian Pigeon" cumulatively cause that Guangzhou 10 kilovolt lines trip 35 times, overlap by 18 times, and cumulatively affect 5.6 tens of thousands of users; in 2018, typhoon "Ai Yunni" caused 61 10kV feeders to trip in a short time in Guangzhou area, wherein the coincidence was unsuccessful by 42, and 15.2 thousands of users were cumulatively affected.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for improving the toughness of a power distribution network, which comprehensively utilizes various resources to formulate a rapid recovery power supply strategy of the power distribution system after an extreme event, realizes a rapid and efficient disaster fault recovery process of the power distribution network and improves the toughness level of the power distribution system; the resistance and the recovery capacity of the power distribution network are improved to the greatest extent, and the loss caused by severe faults caused by typhoon disasters is reduced.
The invention adopts the following technical scheme:
a method for improving toughness of a power distribution network comprises the following steps:
s1, carrying out importance assessment on power distribution network components in typhoon weather, carrying out offline optimization and importance sequencing in a pre-disaster stage, establishing an element fault rate model under the action of storm, sampling the state of a wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sequencing the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the improvement of the robustness of the power distribution network;
s2, a plurality of micro-grids are formed through pre-deployment of the mobile emergency power generation vehicle before the disaster and real-time scheduling after the disaster, an objective function is established by taking the minimum power failure time of important loads in different scenes as a target, an MILP problem is formed through the objective function and constraint conditions, the real-time distribution condition of the mobile emergency power generation vehicle after the disaster is obtained through solving the corresponding problem, and recovery and improvement of the power distribution network are completed.
Specifically, step S1 specifically includes:
simulating component failure rate under storm by adopting an exponential fitting method, determining a relation between the failure rate and the failure probability, defining the sum of power flowing to a demand node as system performance, sampling the system state under the action of storm by using a non-sequential Monte Carlo simulation method, and determining an objective function and model constraint; and (3) solving the MILP problem of each sampling fault configuration to obtain a cumulative distribution function of each component repair time, and sequencing the cumulative distribution function by adopting a Copeland sequencing method.
Further, component failure rate under storm:
Figure BDA0003069980730000021
/>
wherein w (t) is the wind speed at time t, lambda wind (w (t)) is the failure rate of the component, lambda, at a wind speed w (t) norm Is the failure rate of the component under normal conditions; gamma ray 1 ,γ 2 ,γ 3 Fitting coefficients are obtained by fitting curves;
the relationship between failure rate and failure probability is:
Figure BDA0003069980730000022
wherein ,pij Is the failure probability of component (i, j); lambda (lambda) ij Failure rate of component (i, j); t (T) y Is the time associated with the failure rate.
Further, the objective function is:
Figure BDA0003069980730000031
wherein ,
Figure BDA0003069980730000032
is normal system performance, F min Is the minimum for system performance in the event of a disaster.
Further, when a disaster occurs, the damaged components form a set E 'E, E is the set of components, the components in E' are damaged immediately when the disaster occurs, a binary component model is adopted, and the constraint conditions comprise that
The component state constraints are as follows:
Figure BDA0003069980730000033
Figure BDA0003069980730000034
Figure BDA0003069980730000035
wherein ,sij (t) is a binary variable, s ij (T) ∈ {0,1}, t=1, 2,., T, which shows the state of component (i, j) ∈e at time T;
the network capacity constraints are as follows:
Figure BDA0003069980730000036
Figure BDA0003069980730000037
Figure BDA0003069980730000038
Figure BDA0003069980730000039
Figure BDA00030699807300000310
wherein vertices in the network are divided into three classes: generator node V S Transmission node V T And demand node V D Continuous variable f j (t)∈R + Is the power flow received by the demand node j at time t, the continuous variable f ij (t)∈R + Is the power flow transmitted from node i to j at time t,
Figure BDA00030699807300000311
is the transmission capacity of component (i, j) ∈E, P i S Is generator node i epsilon V S Maximum power produced, +.>
Figure BDA0003069980730000041
For the demand node j E V D To all demand nodes, and the power flow must obey the physical constraints of the network;
the personnel path constraints are as follows:
Figure BDA0003069980730000042
/>
Figure BDA0003069980730000043
Figure BDA0003069980730000044
Figure BDA0003069980730000045
Figure BDA0003069980730000046
Figure BDA0003069980730000047
wherein the binary variable x m,n ∈[0,1]Representing repairWhether the tuple moves from m-component to n-component, x if the tuple moves from m-component to n-component m,n Taking 1, otherwise taking 0; m is a large number, dep represents the warehouse,
Figure BDA0003069980730000048
discrete variable +.>
Figure BDA0003069980730000049
Indicating the moment when the serviceman arrives at the component m; binary variable s m (t) represents the state of component m at time t; binary variable f m,τ ∈[0,1]Indicating whether component m was repaired at time t; />
Figure BDA00030699807300000410
Indicating the time required for a service person to repair the assembly; />
Figure BDA00030699807300000411
Record the time f required for the maintenance personnel to travel from component m to component n m,τ Indicating whether component m is repaired at a certain time.
Further, defining the percentile of the cumulative distribution function as Ω -feature, obtaining the keplam score for component m:
Figure BDA00030699807300000412
Figure BDA00030699807300000413
wherein ,qk (m) is the kth percentile of CDFs of component m repair moments; s is S m,n,k Is the Copeland score after the kth comparison of m and n,
Figure BDA00030699807300000414
S m is the kepram fraction of component m, > represents an advantage over.
Specifically, in step S2, an objective function is established with the minimum power failure time of the important load as the objective as follows:
Figure BDA0003069980730000051
wherein α represents the priority weight of the load; p represents the active demand size;
Figure BDA0003069980730000052
interrupt time representing load; beta ikn If it is 0, the load will not resume power supply and will go through T in If the power failure time of (1), the second item
Figure BDA0003069980730000053
Indicating the time required for the load to resume power supply.
Specifically, in step S2, the constraint conditions that are satisfied include a pre-deployment and real-time scheduling constraint, a connection relation constraint of network topology, a line power flow and power balance constraint, and a disaster scene constraint of the power distribution network.
Further, the pre-deployment and real-time scheduling constraints are:
τ smikn ≤β ikn ,τ smikn ≤x smkn ,τ smikn ≥β ikn +x smkn -1
Figure BDA0003069980730000054
Figure BDA0003069980730000055
/>
Figure BDA0003069980730000056
Figure BDA0003069980730000057
wherein ,τsmikn Auxiliary binary variable, beta, introduced for linearizing an objective function ikn To a binary variable indicating whether the load of node i is restored by the power supply of node k in scenario n, x smkn To represent binary variables of real-time dispatch of mobile emergency generator car m from transit location s to node k in scene n, c sm To represent a binary variable of whether the mobile emergency power generation car m is pre-deployed at the transit location s, C s The method comprises the steps that the capacity of a mobile emergency power generation vehicle which can be deployed in a transfer position is limited, M represents the mobile emergency power generation vehicle, M represents a set formed by all the mobile emergency power generation vehicles, S represents the transfer position, S represents a set formed by all the transfer positions, k represents a power distribution network node, and G represents a candidate node set connected with the mobile emergency power generation vehicle;
the connection relation constraint of the network topology is as follows:
Figure BDA0003069980730000061
z kn ＝1
Figure BDA0003069980730000062
v ikn ≤z kn
v kkn ≥z kn
v ikn ≤v jkn ,j＝θ k (i)
Figure BDA0003069980730000063
β ikn ＝v ikn l in
wherein ,zkn To represent whether node k is a feeder node or a binary variable of a mobile emergency power generation car connection in scenario n, v ikn To represent that node i is powered by power connected at node k in scene nElectrical binary variable, v kkn To represent that the load of node k in scenario n is powered by the power source to which node k itself is connected, x ijn Zeta is a binary variable representing whether the line ij is closed or not under scene n k (i, j) is a child node of line ij with respect to node k, θ k (i) Is the parent node, l, of node i with respect to node k in For a binary variable representing whether a load switch of a node i is closed or not under a scene n, h, j represent power distribution network nodes, and F is a set formed by all feeder nodes;
the line power flow and power balance constraint is:
Figure BDA0003069980730000064
Figure BDA0003069980730000065
Figure BDA0003069980730000066
Figure BDA0003069980730000067
Figure BDA0003069980730000068
Figure BDA0003069980730000069
Figure BDA00030699807300000610
/>
Figure BDA00030699807300000611
Figure BDA00030699807300000612
Figure BDA0003069980730000071
Figure BDA0003069980730000072
Figure BDA0003069980730000073
wherein ,
Figure BDA0003069980730000074
active power injected into node i provided by the power supply of node k under scenario n, +. >
Figure BDA0003069980730000075
For reactive power supplied by the power supply of node k in scene n injected into node i, p i For the active demand of the load of node i, q i Reactive demand for the load of node i, +.>
Figure BDA0003069980730000076
For the maximum active force of the mobile emergency generator car m +.>
Figure BDA0003069980730000077
For the maximum reactive power of the mobile emergency generator car m, < >>
Figure BDA0003069980730000078
For the apparent power capacity of line ij, < > j>
Figure BDA0003069980730000079
To represent the connection with node k under scenario nMoving the voltage variable, r, of the emergency generator vehicle-related node i ij Is the resistance of the line ij, x ij Reactance of line ij, +.>
Figure BDA00030699807300000710
For an auxiliary voltage relaxation variable representing node i associated with a mobile emergency generator connected to node k in scenario n, ε is the voltage deviation tolerance, V 0 For rated voltage +.>
Figure BDA00030699807300000711
A set of children nodes for node i with respect to node k;
the disaster scene constraint of the power distribution network is as follows:
Figure BDA00030699807300000712
wherein ,xijn To represent the binary variable of whether the line ij is closed or not in the scene n, LO n For a set of disaster-stricken lines under scene n, n represents different disaster-stricken scenes, (i, j) represents a line between node i and node j.
According to another technical scheme, the system for improving the toughness of the power distribution network comprises the following components:
the robust module is used for carrying out importance assessment on the power distribution network components in typhoon weather, carrying out off-line optimization and importance sequencing in a pre-disaster stage, establishing an element fault rate model under the action of storm, sampling the state of the wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sequencing the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the improvement of the robustness of the power distribution network;
And the recovery module is used for pre-deploying the mobile emergency power generation vehicle before the disaster and forming a plurality of micro-grids through real-time scheduling after the disaster, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the objective function and constraint conditions, and obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster through solving the corresponding problem so as to complete the rapid recovery promotion of the power distribution network.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the method for improving the toughness of the power distribution network, disclosed by the invention, the robustness and the rapidity are improved to improve the capability of a power system for resisting typhoon disasters, so that a rapid and efficient power distribution network disaster fault recovery process is realized. The robustness of the power distribution network is improved by carrying out importance evaluation on the components of the power system before disaster, and a component failure rate model under the action of storm is established. According to the model, the system states under the storm effect can be sampled by a non-sequential Monte Carlo simulation method, an optimal repair model is obtained by solving a component repair sequence optimization model considering unit dispatching for each system state, finally, a distribution function of element repair time can be obtained through full system state sampling, and the importance of the components is sequenced by adopting a Copeland sequencing method, so that compared with the previous research, the method has the advantages that the elasticity is considered and the dispatching of maintenance personnel is considered; according to the identified weak links and the specific situations of disaster occurrence, a random planning model is established through pre-disaster emergency configuration and post-disaster real-time scheduling of the mobile emergency power generation vehicle, the deployment position of the mobile power generation vehicle is optimized before typhoon occurs, the power generation vehicle is scheduled to carry out rapid reconstruction of distribution network topology by combining measures such as topology reconstruction and the like after typhoon occurs, a micro-grid is formed to supply power for important loads, recovery of important loads can be effectively guaranteed, recovery time of distribution network faults is shortened, rapidity is improved, and toughness of the distribution network is further improved.
Further, the importance evaluation of the components in step S1 has an important meaning for improving the disaster-resistant capability, because it plays a vital role in reinforcing the grid structure, designing the recovery strategy, and improving the resource allocation efficiency of disaster prevention and reduction; the results of component importance assessment can provide us with an efficient and economical reinforcement strategy to improve the recovery capacity of the power system. Common extreme hazards, such as storms, often result in multiple component failures at the same time, and these damaged components cannot be serviced at the same time due to maintenance personnel and resource limitations. Obviously, the order of repair affects the recovery time, and therefore the importance of the component should be assessed. Component importance assessment may provide an effective repair solution. In terms of component importance assessment, most research has focused on reliability areas that consider typical failures, with little consideration for elasticity-based component importance assessment. Therefore, the patent proposes a component importance assessment method for improving the storm resistance of the power system. The method can provide a component importance ordering method for improving the recovery capacity of the power system in advance, so that an optimal recovery strategy can be quickly obtained without long-time calculation when a disaster occurs.
Further, wind disaster is a natural disaster with relatively high frequency in coastal areas, and average economic loss caused by power failure due to wind disaster is very huge each year, so that research on effective recovery strategies has important meaning of a power system under storm to improve the recovery capability of the power system. To accurately simulate the recovery process, I need to simulate the effect of storms on system conditions by combining storm speeds with failure rates. During storms, as the tree pressure increases, the tree is more likely to fall on overhead lines and damage the lines. In addition, friction between the tower and wind, and between the wire and wind increases, directly causing the tower and wire to drop or contact other objects. Therefore, wind has great influence on the failure rate of the power transmission line, and the establishment of the component failure rate under storm and the relationship between the failure rate and the failure probability can help us to evaluate each component more systematically, so that the power supply is recovered to a greater extent.
Furthermore, an optimal recovery model considering the scheduling of maintenance personnel is established based on a complex network theory with the aim of finding out the optimal recovery sequence of the components and improving the recovery capacity. The calculation results of different positions of the maintenance station can provide meaningful planning references for designers;
Further, component state constraints and network capacity constraints place the most fundamental constraints on post-disaster energy balance. After the disaster occurs, the maintenance personnel of the warehouse will repair the damaged components, and in order to make the evaluation result of the importance of the components more convincing, we consider the travel distance of the maintenance personnel. The restoring force in this patent is related to the overall restoring process, so we cannot only consider the contribution of the components. If we first repair components that contribute significantly to the system but are far from the warehouse, we may get less flexibility throughout the process. Thus, the time required for a serviceman to travel between two damaged components during repair is considered.
Further, by solving the MILP problem of each sampling fault configuration, a cumulative distribution function of each component repair moment can be obtained. To order the importance of the components, a Copeland ordering method was introduced. The kepram ranking is a non-parametric Kong Duosai method commonly used in the politics field. This approach does not require much information about the data and runs in a pool of candidates with each object having an X feature. By comparing objects with different X features in the candidate library in pairs, the scores of all the candidate objects can be calculated, and the candidate objects are ranked according to the scores. This patent employs a modified Copeland method that can be used to rank CDFs. Defining the percentile of CDF as X feature, so that the kepram score (Sm) of component m can be obtained, thereby completing the ranking of the importance of the components;
Further, the power distribution system is seriously damaged under extreme disasters, so that a large-area power failure of a user is caused. Rapid restoration of power supply is one of the key requirements of a flexible power grid. The mobile emergency power generation vehicle is used as a key flexible resource for rapid power supply recovery of a power distribution system, and the current utilization efficiency is low. By taking the minimum power failure time of important loads as a target in a pre-deployment and real-time scheduling model, on one hand, more and more important loads can be recovered in the shortest time after extreme disasters, such as governments, hospitals, fire-fighting institutions and the like; on the other hand, the utilization rate of the mobile emergency power generation vehicle can be improved, so that the mobile emergency power generation vehicle plays a larger role after extreme disasters.
Furthermore, the mobile emergency power generation vehicle is accessed to the power distribution network as a distributed power supply to carry out topology rapid reconstruction, and a micro-grid is formed to restore power supply for loads in the area, so that the operation constraint of the power distribution network, namely the load flow and power balance constraint, needs to be met. In order to ensure that the radial distribution network structure needs to meet the topology constraint of the distribution network. Different disaster-affected scenes can be formed through the constraint of the disaster-affected scenes of the power distribution network, so that the mobile emergency power generation vehicle is pre-deployed more pertinently. The pre-deployment constraint can pre-deploy the mobile emergency power generation vehicle before the disaster and avoid violating capacity limits of the transfer station. The real-time scheduling constraint ensures that the mobile emergency power generation cars are scheduled from the transfer station to the candidate nodes after the disaster, and simultaneously ensures that each candidate node is allocated with one mobile emergency power generation car at most for improving the utilization rate.
In conclusion, the importance evaluation is carried out on the elements before disaster, so that the fault scale caused by extreme weather can be effectively reduced, and the robustness of the power distribution network is improved; by deploying the mobile emergency power generation vehicle before the disaster and carrying out real-time scheduling after the disaster, the fault recovery time of the power distribution network is shortened, and the rapidity of power distribution network recovery is realized.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic diagram of two dimensions of a power distribution network lift;
FIG. 2 is an abstract topology network diagram of an IEEE 14 node system;
FIG. 3 is a graph of transfer functions of five exemplary component repair moments;
FIG. 4 is a result diagram of a Copeland ordering method in an IEEE 14 node system;
FIG. 5 is a traffic information graph of the test system;
fig. 6 is a topology diagram of a distribution network of the test system and a post-disaster micro-grid partition result diagram.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
Referring to fig. 1, the present invention provides a method for improving toughness of a power distribution network, where the improvement of toughness of the power distribution network can be divided into two dimensions, namely, improvement of robustness of the power distribution network and improvement of recovery rapidity of the power distribution network; the robustness of the power distribution network is improved, and the importance evaluation is carried out on the elements before disaster so as to reduce the fault scale caused by extreme weather; the recovery rapidity of the power distribution network is improved, and the fault recovery time of the power distribution network is shortened through the deployment of the mobile emergency power generation vehicle before the disaster and the real-time scheduling after the disaster.
The method for improving the toughness of the power distribution network comprises two aspects, namely, the robustness of the power distribution network is improved by carrying out importance evaluation on power system components before disaster, and the fault scale caused by extreme weather is reduced; secondly, the deployment position of the mobile power generation vehicle is optimized before disaster by improving the rapidity of the power distribution network, and the power generation vehicle is scheduled to form a micro-grid by combining measures such as topology reconstruction and the like to supply power to important loads, so that the recovery time of the power distribution network faults is shortened, and the toughness of the power distribution network is further improved. The method specifically comprises the following steps:
s1, evaluating importance of a power distribution network component in typhoon weather is conducted, and robustness of the power distribution network is improved;
Comprising two stages: the sequencing result is obtained offline in the pre-disaster stage, and the optimal recovery strategy is conveniently and rapidly obtained in the recovery stage; the method comprises the following steps:
offline optimization and importance ranking are carried out at the pre-disaster stage, an element fault rate model under the action of storm is established, the state of the wind power generation system is sampled by adopting a non-sequential Monte Carlo simulation method, and then an optimal recovery strategy model is established and the importance of components is ranked; when a disaster occurs, a failure condition is acquired, and elements with high importance are recovered preferentially according to the previously determined order.
S101, wind has great influence on the failure rate of the power transmission line, and an exponential fitting method is adopted to simulate the failure rate of components under storm wind:
Figure BDA0003069980730000131
where w (t) is the wind speed at time t. Lambda (lambda) wind (w (t)) is the failure rate of the component, lambda, at a wind speed w (t) norm Is the failure rate of the component under normal conditions; gamma ray 1 ,γ 2 ,γ 3 The fitting coefficient is obtained from a fitting curve.
Relationship between failure rate and failure probability:
Figure BDA0003069980730000132
wherein ,pij Is the failure probability of component (i, j); lambda (lambda) ij Failure rate of component (i, j); t (T) y Is the time associated with the failure rate.
S102, sampling the system state under the storm action by using a non-sequential Monte Carlo simulation method
For each system state, an optimal recovery strategy model is obtained by solving a component maintenance sequence optimization model considering unit scheduling, when a disaster occurs, damaged components form a set E ', and the components in E' are assumed to be damaged immediately after the disaster occurs, so that the system performance reaches the minimum. The goal of the model is to find the repair order of the failed element, so that the system obtains the maximum recovery capacity in the recovery process.
The sum of the power flowing to the demand node is defined as the system performance:
Figure BDA0003069980730000133
/>
wherein ,fj And (t) is the power flow received by the demand node j at time t.
In order to obtain maximum elasticity, the objective function is determined as:
Figure BDA0003069980730000141
wherein ,
Figure BDA0003069980730000142
is normal system performance, F min It is the disaster that occurs that the system performance reaches a minimum.
In the optimal restoration policy model based on the maximum restoration force, constraint conditions include component states, network capacity, and personnel path constraints.
Component state constraints
Adopts a binary component model：s ij (t)∈[0,1]T E1, 2, T, it shows the component at time T (i, j) state of E, the constraints are:
Figure BDA0003069980730000143
Figure BDA0003069980730000144
Figure BDA0003069980730000145
equation (5) shows s ij (t) is a binary variable; (6) indicating that once repaired, the component will continue to operate; (7) Each component in E' is shown to have been damaged at the beginning of the restoration process.
Network capacity constraints
The limitations related to node and component capacity are:
Figure BDA0003069980730000146
Figure BDA0003069980730000147
Figure BDA0003069980730000148
Figure BDA0003069980730000149
Figure BDA00030699807300001410
wherein the continuous variable f j (t)∈R + Is the power flow received by the demand node j at time t; continuous variable f ij (t)∈R + Is the power flow transmitted from node i to j at time t; equations (8), (9) (10) are typical constraints for generator nodes, transmission nodes, and demand nodes: (8) Displaying that the power generated by the generator node cannot exceed the maximum capacity; (9) and (10) are energy balance constraints; (11) Indicating that the actual power provided to the demand node cannot exceed its demand; (12) limiting the power flow transmitted through the component.
Personnel path constraints
After the disaster occurs, maintenance personnel of the warehouse will repair the damaged components. In order to make the component importance evaluation result more convincing, the travel distance of the maintenance personnel is considered. Suppose that a serviceman starts from a warehouse. Limitations related to distance and travel time are:
Figure BDA0003069980730000151
Figure BDA0003069980730000152
Figure BDA0003069980730000153
Figure BDA0003069980730000154
Figure BDA0003069980730000155
Figure BDA0003069980730000156
Figure BDA0003069980730000157
wherein the binary variable x m,n E (0, 1) indicates whether the repair group moves from m-component to n-component, if the repair group moves from m-component to n-component, x m,n =1, otherwise take 0; m is a large number; dep represents a warehouse;
Figure BDA0003069980730000158
indicating the time when the maintenance personnel arrives at the warehouse; discrete variable->
Figure BDA0003069980730000159
Indicating the moment when the serviceman arrives at the component m; binary variable s m (t) represents the state of component m at time t; binary variable f m,t ∈[0,1]Indicating whether component m was repaired at time t; />
Figure BDA00030699807300001510
Indicating how long it takes for a service person to repair the assembly; />
Figure BDA00030699807300001511
Record how long it takes for the maintenance personnel to go from m to n.
Equations (13) and (14) show that the serviceman can only reach one component once, leave once. Equation (15) ensures that the path of the maintenance personnel is continuous. The maintenance personnel will spend after reaching the point m
Figure BDA0003069980730000161
Repair the assembly and then they will spend +>
Figure BDA0003069980730000162
From m to n. The large M method ensures that the routes are continuous, since, assuming that the routes are not continuous, there must be at least one x m,n =0 and thus results in +.>
Figure BDA0003069980730000163
Large, at this time, solution is impossibleCan be optimal. Equation (16) shows that the serviceman leaves the warehouse at the beginning of the flow. Equation (17) shows that each damaged component can only be repaired once. Equation (18) establishes f m,t And->
Figure BDA0003069980730000164
A relationship between; equation (19) shows that the assembly works well after repair.
The time when the damaged component m is repaired is denoted as T m Calculated by the following formula:
Figure BDA0003069980730000165
s103, after sampling the state of the wind power generation system, obtaining a cumulative distribution function of repair time of each component by solving MILP problems of each sampling fault configuration.
To order the importance of the components, a modified Copeland ordering method was introduced for ordering the cumulative distribution function CDF (Cumulative Distribution Function), defining the percentile of CDF as Ω -feature, thus obtaining the copland score (Sm) for component m:
Figure BDA0003069980730000166
wherein ,qk (m) is the kth percentile of CDFs of component m repair moments; s is S m,n,k Is the Copeland score after the kth comparison of m and n,
Figure BDA0003069980730000167
S m the symbol ">" represents "better" which, in this example, represents "precedent" for the kepram score of component m; the symbol "<" means "posterior to".
S2, forming a plurality of micro-grids through pre-deployment mobile emergency power generation vehicles before disaster and real-time scheduling after disaster, and shortening the fault recovery time of the power distribution network;
s201, the pre-deployment problem is a two-stage random optimization problem based on a scene, and the pre-deployment decision is determined by a plurality of real-time allocation problems corresponding to the considered distribution network and road damage.
In extreme weather, continuous power supply of important loads is ensured, so that the minimum power failure time of the important loads in different scenes is taken as a target, and the objective function is as follows:
Figure BDA0003069980730000171
wherein Ω represents different disaster-stricken scenes; alpha represents the priority weight of the load; p represents the active demand size;
Figure BDA0003069980730000172
interrupt time representing load; beta ikn If 0, the load will not restore power and will go through T in If the power failure time of (1), the second item in square brackets ++>
Figure BDA0003069980730000173
Indicating the time required for the load to resume power supply.
The constraint conditions to be satisfied are: pre-deployment and real-time scheduling constraint of a mobile emergency power generation vehicle, connection relation constraint of network topology, line power flow and power balance constraint and disaster scene constraint of a power distribution network; finally, an MILP problem is formed through a target function and constraint conditions, and the pre-deployment problem can be solved by using a scene decomposition algorithm.
The constraint conditions are as follows:
pre-deployment and real-time scheduling constraints:
τ smikn ≤β ikn ,τ smikn ≤x smkn ,τ smikn ≥β ikn +x smkn -1 (23)
Figure BDA0003069980730000174
connection relation constraint of network topology:
Figure BDA0003069980730000175
Figure BDA0003069980730000181
/>
line flow and power balance constraint:
Figure BDA0003069980730000182
Figure BDA0003069980730000183
Figure BDA0003069980730000184
Figure BDA0003069980730000185
Figure BDA0003069980730000186
Figure BDA0003069980730000187
the distribution network is constrained by disaster scene:
Figure BDA0003069980730000188
because the post-disaster scene is clear, different scenes do not need to be considered in the real-time scheduling model of the mobile emergency power generation car, and the objective function is put forward to take the minimum power failure time of important load as the objective:
Figure BDA0003069980730000189
the constraint conditions to be met can be added with the constraint of the maximum power-off time acceptable by some key loads, the constraint of the utilization rate of the mobile emergency power generation vehicle and the like besides the constraint.
And finally, forming an MILP problem through an objective function and constraint conditions, and obtaining the real-time distribution condition of the post-disaster mobile emergency power generation vehicle by solving the corresponding problem.
In still another embodiment of the present invention, a system for improving toughness of a power distribution network is provided, where the system can be used to implement the method for improving toughness of a power distribution network, and specifically, the system for improving toughness of a power distribution network includes a robust module and a recovery module.
The robust module performs importance assessment on power distribution network components in typhoon weather, performs offline optimization and importance sequencing in a pre-disaster stage, establishes an element failure rate model under the action of storm, adopts a non-sequential Monte-Card Luo Fangzhen method to sample the state of a wind power generation system, establishes an optimal recovery strategy model and sequences the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the improvement of the robustness of the power distribution network;
And the recovery module is used for pre-deploying the mobile emergency power generation vehicle before the disaster and forming a plurality of micro-grids through real-time scheduling after the disaster, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the objective function and constraint conditions, and obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster through solving the corresponding problem so as to complete the rapid recovery promotion of the power distribution network.
In yet another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor of the embodiment of the invention can be used for the operation of the method for improving the toughness of the power distribution network, and comprises the following steps:
Carrying out importance assessment on power distribution network components in typhoon weather, carrying out offline optimization and importance sequencing in a pre-disaster stage, establishing an element fault rate model under the action of storm, sampling the state of a wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sequencing the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the improvement of the robustness of the power distribution network; the method comprises the steps of pre-deploying a mobile emergency power generation vehicle before a disaster and performing real-time scheduling after the disaster to form a plurality of micro-grids, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem by the objective function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster by solving the corresponding problem, and completing the recovery rapidity improvement of the power distribution network.
In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the method for improving toughness of a power distribution network of the above embodiments; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
carrying out importance assessment on power distribution network components in typhoon weather, carrying out offline optimization and importance sequencing in a pre-disaster stage, establishing an element fault rate model under the action of storm, sampling the state of a wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sequencing the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the improvement of the robustness of the power distribution network; the method comprises the steps of pre-deploying a mobile emergency power generation vehicle before a disaster and performing real-time scheduling after the disaster to form a plurality of micro-grids, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem by the objective function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster by solving the corresponding problem, and completing the recovery rapidity improvement of the power distribution network.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as provided in the accompanying drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The present invention uses an IEEE-14 node algorithm for element importance assessment, and an IEEE-14 node system including 14 nodes and 20 lines is converted into a topology network composed of nodes and edges, as shown in fig. 2. Nodes are classified into three types, generator nodes, demand nodes and transmission nodes. The strategy and time of service personnel travel is affected by the distance between the components and the entire IEEE 14 bus network is divided into six areas. The distance between two adjacent areas is defined as a distance unit.
Table 1 table initial parameters of IEEE 14 bus system
Figure BDA0003069980730000221
After multiple simulations (1000 times here), CDFs of 20 component repair moments can be obtained. Fig. 3 shows CDFs for repair moments of five representative components. It can be seen that the repair time for component <6, 11> (line between node 6 and node 11) is always less than 8 and the repair time for component <4,7> is always greater than 12. Obviously, component <6, 11> may be considered more important than component <4,7>, because as component <6, 11> is repaired earlier than component <4,7>, the system will obtain a greater elasticity value.
However, the relative importance of not all components may be so intuitively judged. For example, the importance relationship between components <6, 12> and components <6, 13> is difficult to judge because their distribution functions intersect.
Thus, the importance of such components may be ranked using the keplaan ranking method. FIG. 4 shows the Copland score for each component in the IEEE-14 bus system. As can be seen from fig. 4, element <3,4> has the highest Copland score, while element <4,7> has the lowest Copland score. There are two types of components that score higher:
(1) a component connecting the two regions, such as <4,9>, <5,6>, <7,9>;
(2) Demand nodes and generator nodes near the warehouse, such as <3,4>, <6, 11>.
There are two types of components that score lower:
(1) components between generator nodes, such as <1,2>, <2,3>;
(2) there are multiple components between two nodes. These components have a lower score such as <2,4>, <2,5>. Components with high copperd scores should have higher priority in the recovery process, which may make the overall recovery process more efficient.
Fig. 5 and 6 are test systems for improving the recovery rapidity of a post-disaster power distribution network according to the present invention. Fig. 5 shows a traffic geographical information diagram, which includes 51 intersections, 82 sides and 3 transfer stations S1, S2, S3. Fig. 6 shows a power distribution system for 114 nodes, table 2 lists the available capacity of the mobile emergency power generation vehicle.
TABLE 2 available capacity of MEGs
Figure BDA0003069980730000231
The pre-deployment scenario is shown in the second column of table 3.
TABLE 3 MEGs Pre-deployment and real-time scheduling results
Figure BDA0003069980730000232
After an extreme disaster, the line with the cross shown in fig. 6 is damaged, the real-time dispatching situation of the mobile emergency power generation car is shown in the third column of table 3, and the micro-grid partitioning situation is shown in the shadow in fig. 6. MEG1, for example, is pre-deployed at a transfer station S2, and is scheduled to node 403 in real time after the disaster, forming load restoration power for the microgrid connection 403 and 404.
In summary, the method and the system for improving the toughness of the power distribution network, provided by the invention, consider not only elasticity, but also scheduling of maintenance personnel. In each simulation process, we solve one MILP, and get the cumulative distribution function of the repair moment of each component through multiple simulation processes. The components are then ordered using the Copeland ordering method. Once the grade of the component is obtained, in the event of a disaster, the system operator may schedule maintenance personnel to service the damaged component according to the grade order. Those components with higher ranking scores will repair earlier. The strategies can be implemented on line, so that the recovery speed of the power grid after the disaster occurs is greatly increased; due to the continued damage to the distribution network components after extreme disasters, many users often experience long-term blackouts. Mobile emergency power generation vehicles are a flexible resource for quickly restoring power supply. The invention uses the mobile emergency power generation vehicle as a distributed power supply to schedule, and the load power supply is recovered by reconstructing the power distribution network topology into a plurality of micro-grids. The method is realized through a two-stage scheduling framework of pre-deployment and real-time distribution. The examples verify the validity of the proposed method. The mobile emergency power generation vehicle dispatching method can reduce large-scale power failure after extreme disasters occur, and can be better used for toughness emergency response to the extreme disasters.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (6)

1. The method for improving the toughness of the power distribution network is characterized by comprising the following steps of:
S1, carrying out importance assessment on power distribution network components in typhoon weather, carrying out offline optimization and importance sorting in a pre-disaster stage, establishing a component failure rate model under the action of storm, sampling the state of a wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sorting the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering components with high importance according to a determined sequence to realize the robustness improvement of the power distribution network, wherein the method specifically comprises the following steps:
simulating component failure rate under storm by adopting an exponential fitting method, determining a relation between the failure rate and the failure probability, defining the sum of power flowing to a demand node as system performance, sampling the system state under the action of storm by using a non-sequential Monte Carlo simulation method, and determining an objective function and model constraint; obtaining a cumulative distribution function of each component repair time by solving MILP problems of each sampling fault configuration, and sequencing the cumulative distribution function by adopting a Copeland sequencing method;
when a disaster occurs, the damaged components form a set E 'E, E is the set of components, the components in E' are damaged immediately when the disaster occurs, a binary component model is adopted, and the constraint conditions comprise:
The component state constraints are as follows:
Figure FDA0004107673580000011
Figure FDA0004107673580000012
Figure FDA0004107673580000013
wherein ,sij (t) is a binary variable, s ij (T) ∈ {0,1}, t=1, 2,., T, which shows the state of component (i, j) ∈e at time T;
the network capacity constraints are as follows:
Figure FDA0004107673580000014
Figure FDA0004107673580000015
Figure FDA0004107673580000016
Figure FDA0004107673580000017
Figure FDA0004107673580000018
wherein vertices in the network are divided into three classes: generator node V S Transmission node V T And demand node V D Continuous variable f j (t)∈R + Is the power flow received by the demand node j at time t, the continuous variable f ij (t)∈R + Is the power flow transmitted from node i to j at time t,
Figure FDA0004107673580000021
is the transmission capacity of component (i, j) ∈E, P i S Is generator node i epsilon V S Maximum power produced, +.>
Figure FDA0004107673580000022
For the demand node j E V D To all demand nodes, and the power flow must obey the physical constraints of the network;
the personnel path constraints are as follows:
Figure FDA0004107673580000023
Figure FDA0004107673580000024
Figure FDA0004107673580000025
Figure FDA0004107673580000026
/>
Figure FDA0004107673580000027
Figure FDA0004107673580000028
wherein the binary variable x m,n ∈[0,1]Indicating whether the repair group moves from m-component to n-component, x if the repair group moves from m-component to n-component m,n Taking 1, otherwise taking 0; m is a large number, dep represents the warehouse,
Figure FDA0004107673580000029
discrete variable +.>
Figure FDA00041076735800000210
Indicating the moment when the serviceman arrives at the component m; binary variable s m (t) represents the state of component m at time t; binary variable f m,τ ∈[0,1]Indicating whether component m was repaired at time t; / >
Figure FDA00041076735800000211
Indicating the time required for a service person to repair the assembly; />
Figure FDA00041076735800000212
Record the time required for a serviceman to travel from component m to component n, f m,t Representing the repair status of component m at time t;
s2, forming a plurality of micro-grids through pre-deployment mobile emergency power generation vehicles before the disaster and real-time scheduling after the disaster, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the objective function and constraint conditions, obtaining the real-time distribution condition of the post-disaster mobile emergency power generation vehicles through solving the corresponding problem, and finishing recovery and lifting of the power distribution network, wherein the satisfied constraint conditions comprise pre-deployment and real-time scheduling constraint, connection relation constraint of network topology, line power flow and power balance constraint and power distribution network disaster scene constraint; the pre-deployment and real-time scheduling constraints are:
τ smikn ≤β ikn ,τ smikn ≤x smkn ,τ smikn ≥β ikn +x smkn -1
Figure FDA0004107673580000031
Figure FDA0004107673580000032
Figure FDA0004107673580000033
Figure FDA0004107673580000034
wherein ,τsmikn Auxiliary binary variable, beta, introduced for linearizing an objective function ikn To indicate whether the load of node i in scenario n is restored by the power supply of node kElectrical binary variable x smkn To represent binary variables of real-time dispatch of mobile emergency generator car m from transit location s to node k in scene n, c sm To represent a binary variable of whether the mobile emergency power generation car m is pre-deployed at the transit location s, C s The capacity limit of the mobile emergency power generation vehicle which can be deployed in the transfer position is defined, M represents the mobile emergency power generation vehicle, M represents a set formed by all the mobile emergency power generation vehicles, S represents the transfer position, S represents a set formed by all the transfer positions, k represents a power distribution network node, and G represents a candidate node set connected with the mobile emergency power generation vehicle;
the connection relation constraint of the network topology is as follows:
Figure FDA0004107673580000035
z kn ＝1
Figure FDA0004107673580000036
v ikn ≤z kn
v kkn ≥z kn
v ikn ≤v jkn ,j＝θ k (i)
Figure FDA0004107673580000037
β ikn ＝v ikn l in
wherein ,zkn To represent whether node k is a feeder node or a binary variable of a mobile emergency power generation car connection in scenario n, v ikn To represent a binary variable in scene n that node i is powered by a power supply connected at node k, v kkn To represent that the load of node k in scenario n is powered by the power source to which node k itself is connected, x ijn ζ is a binary variable representing whether line ij is closed in scene n k (i, j) is a line about node kChild node of road ij, θ k (i) Is the parent node, l, of node i with respect to node k in For a binary variable representing whether a load switch of a node i is closed or not under a scene n, h, j represent power distribution network nodes, and F is a set formed by all feeder nodes;
the line power flow and power balance constraint is:
Figure FDA0004107673580000041
Figure FDA0004107673580000042
Figure FDA0004107673580000043
Figure FDA0004107673580000044
Figure FDA0004107673580000045
Figure FDA0004107673580000046
Figure FDA0004107673580000047
Figure FDA0004107673580000048
Figure FDA0004107673580000049
Figure FDA00041076735800000410
Figure FDA00041076735800000411
Figure FDA00041076735800000412
wherein ,
Figure FDA00041076735800000413
active power injected into node i provided by the power supply of node k under scenario n, +. >
Figure FDA00041076735800000414
For reactive power injected into node i, p, supplied by the power supply of node k in scenario n i For the active demand of the load of node i, q i Reactive demand for the load of node i, +.>
Figure FDA00041076735800000415
For the maximum active force of the mobile emergency generator car m +.>
Figure FDA00041076735800000416
For the maximum reactive power of the mobile emergency generator car m, < >>
Figure FDA00041076735800000417
For the apparent power capacity of line ij, < > j>
Figure FDA00041076735800000418
To represent the voltage variation of node i associated with a mobile emergency generator connected to node k in scenario n, r ij For the resistance of line ij, x ij Reactance of line ij, +.>
Figure FDA00041076735800000419
For an auxiliary voltage relaxation variable representing node i associated with a mobile emergency generator connected to node k in scenario n, ε is the voltage deviation tolerance, V 0 At rated voltage v ikn As binary variable, 1, y if node i belongs to the micro-grid formed by the mobile emergency power generation cars connected at node k in scenario n smkn As binary variable, if the emergency power generation car m moving in the scene n moves from the transit position s to k, the emergency power generation car m is 1, V R For reference voltage, +.>
Figure FDA0004107673580000051
A set of children nodes for node i with respect to node k; />
The disaster scene constraint of the power distribution network is as follows:
Figure FDA0004107673580000052
wherein ,xijn To represent the binary variable of whether the line ij is closed or not in the scene n, LO n For a set of disaster-stricken lines under scene n, n represents different disaster-stricken scenes, (i, j) represents a line between node i and node j.
2. The method for improving toughness of a power distribution network according to claim 1, wherein in step S1, component failure rate under storm:
Figure FDA0004107673580000053
wherein w (t) is the wind speed at time t, lambda wind (w (t)) is the failure rate of the component, lambda, at a wind speed w (t) norm Is the failure rate of the component under normal conditions; gamma ray 1 ,γ 2 ,γ 3 Fitting coefficients are obtained by fitting curves;
the relationship between failure rate and failure probability is:
Figure FDA0004107673580000054
wherein ,pij Is the failure probability of component (i, j); lambda (lambda) ij Failure rate of component (i, j); t (T) y Is the time associated with the failure rate.
3. The method for improving toughness of a power distribution network according to claim 1, wherein in step S1, the objective function is:
Figure FDA0004107673580000055
wherein ,
Figure FDA0004107673580000056
is normal system performance, F min To minimize system performance in the event of a disaster, f j (T) is a continuous variable, T is the duration of the recovery process, V D Is a demand node.
4. The method for improving toughness of a power distribution network according to claim 1, wherein in step S1, a percentile of cumulative distribution functions is defined as Ω feature, and a keplam score of the component m is obtained:
Figure FDA0004107673580000061
wherein ,qk (m) is the kth percentile of CDFs of component m repair moments; s is S m,n,k Is the Copeland score after the kth comparison of m and n,
Figure FDA0004107673580000062
S m Is the kepram fraction of component m, > represents an advantage over.
5. The method for improving toughness of a power distribution network according to claim 1, wherein in step S2, an objective function is established with the objective of minimum blackout time of an important load as follows:
Figure FDA0004107673580000063
wherein α represents the priority weight of the load; p represents the active demand size;
Figure FDA0004107673580000064
interrupt time representing load; beta ikn If it is 0, the load will not resume power supply and will go through T in If the power failure time of (1), the second item
Figure FDA0004107673580000065
Representing the time required for load recovery power supply, i is a node, B is a node set of a power distribution network, k is a node of the power distribution network, F is a set of all feeder nodes, G is a candidate node set for connection of a mobile emergency power generation vehicle, n is different disaster scenes, S is a transfer position, S is a set of all transfer positions, M is a mobile emergency power generation vehicle, M is a set of all mobile emergency power generation vehicles, x is a set of all mobile emergency power generation vehicles smkn For a binary variable, t, of a mobile emergency power generation vehicle m from a transit position s to a node k in real time under a scene n skn The time from transit location s to node k is the next mobile emergency generator car in scenario n.
6. A system for improving toughness of a power distribution network, comprising:
the robust module is used for carrying out importance assessment on the power distribution network components in typhoon weather, carrying out offline optimization and importance sorting in a pre-disaster stage, establishing an element fault rate model under the action of storm, sampling the state of the wind power generation system by adopting a non-sequential Monte Carlo simulation method, establishing an optimal recovery strategy model and sorting the importance of the components; when a disaster occurs, acquiring a fault condition, and preferentially recovering elements with high importance according to a determined sequence to realize the robustness improvement of the power distribution network, wherein the method specifically comprises the following steps:
Simulating component failure rate under storm by adopting an exponential fitting method, determining a relation between the failure rate and the failure probability, defining the sum of power flowing to a demand node as system performance, sampling the system state under the action of storm by using a non-sequential Monte Carlo simulation method, and determining an objective function and model constraint; obtaining a cumulative distribution function of each component repair time by solving MILP problems of each sampling fault configuration, and sequencing the cumulative distribution function by adopting a Copeland sequencing method;
when a disaster occurs, the damaged components form a set E 'E, E is the set of components, the components in E' are damaged immediately when the disaster occurs, a binary component model is adopted, and the constraint conditions comprise:
the component state constraints are as follows:
Figure FDA0004107673580000071
Figure FDA0004107673580000072
Figure FDA0004107673580000073
wherein ,sij (t) is a binary variable, s ij (T) ∈ {0,1}, t=1, 2,., T, which shows the state of component (i, j) ∈e at time T;
the network capacity constraints are as follows:
Figure FDA0004107673580000074
Figure FDA0004107673580000075
Figure FDA0004107673580000076
Figure FDA0004107673580000077
Figure FDA0004107673580000078
wherein vertices in the network are divided into three classes: generator node V S Transmission node V T And demand node V D Continuous variable f j (t)∈R + Is the power flow received by the demand node j at time t, the continuous variable f ij (t)∈R + Is the power flow transmitted from node i to j at time t,
Figure FDA0004107673580000079
Is the transmission capacity of component (i, j) ∈E, P i S Is generator node i epsilon V S Maximum power produced, +.>
Figure FDA00041076735800000710
For the demand node j E V D To all demand nodes, and the power flow must obey the physical constraints of the network;
the personnel path constraints are as follows:
Figure FDA0004107673580000081
Figure FDA0004107673580000082
/>
Figure FDA0004107673580000083
Figure FDA0004107673580000084
Figure FDA0004107673580000085
Figure FDA0004107673580000086
wherein the binary variable x m,n ∈[0,1]Indicating whether the repair group moves from m-component to n-component, x if the repair group moves from m-component to n-component m,n Taking 1, otherwise taking 0; m is a large number, dep represents the warehouse,
Figure FDA0004107673580000087
discrete variable +.>
Figure FDA0004107673580000088
Indicating the moment when the serviceman arrives at the component m; binary variable s m (t) represents the state of component m at time t; binary variable f m,τ ∈[0,1]Indicating whether component m was repaired at time t; />
Figure FDA0004107673580000089
Indicating the time required for a service person to repair the assembly; />
Figure FDA00041076735800000810
Record the time required for a serviceman to travel from component m to component n, f m,t Representing the repair status of component m at time t;
the recovery module is used for forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation vehicle before the disaster and real-time scheduling after the disaster, establishing an objective function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the objective function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation vehicle after the disaster through solving the corresponding problem, and completing the rapid promotion of the recovery of the power distribution network, wherein the satisfied constraint conditions comprise pre-deployment and real-time scheduling constraint, connection relation constraint of network topology, line power flow and power balance constraint and disaster scene constraint of the power distribution network; the pre-deployment and real-time scheduling constraints are:
τ smikn ≤β ikn ,τ smikn ≤x smkn ,τ smikn ≥β ikn +x smkn -1
Figure FDA00041076735800000811
Figure FDA00041076735800000812
Figure FDA00041076735800000813
Figure FDA00041076735800000814
wherein ,τsmikn Auxiliary binary variables introduced for linearizing an objective function，β ikn To a binary variable indicating whether the load of node i is restored by the power supply of node k in scenario n, x smkn To represent binary variables of real-time dispatch of mobile emergency generator car m from transit location s to node k in scene n, c sm To represent a binary variable of whether the mobile emergency power generation car m is pre-deployed at the transit location s, C s The capacity limit of the mobile emergency power generation vehicle which can be deployed in the transfer position is defined, M represents the mobile emergency power generation vehicle, M represents a set formed by all the mobile emergency power generation vehicles, S represents the transfer position, S represents a set formed by all the transfer positions, k represents a power distribution network node, and G represents a candidate node set connected with the mobile emergency power generation vehicle;
the connection relation constraint of the network topology is as follows:
Figure FDA0004107673580000091
z kn ＝1
Figure FDA0004107673580000092
v ikn ≤z kn
v kkn ≥z kn
v ikn ≤v jkn ,j＝θ k (i)
Figure FDA0004107673580000093
β ikn ＝v ikn l in
wherein ,zkn To represent whether node k is a feeder node or a binary variable of a mobile emergency power generation car connection in scenario n, v ikn To represent a binary variable in scene n that node i is powered by a power supply connected at node k, v kkn To represent that the load of node k in scenario n is powered by the power source to which node k itself is connected, x ijn To represent a fieldBinary variable, ζ, of whether or not the line ij under scene n is closed k (i, j) is a child node of line ij with respect to node k, θ k (i) Is the parent node, l, of node i with respect to node k in For a binary variable representing whether a load switch of a node i is closed or not under a scene n, h, j represent power distribution network nodes, and F is a set formed by all feeder nodes;
the line power flow and power balance constraint is:
Figure FDA0004107673580000094
Figure FDA0004107673580000095
Figure FDA0004107673580000101
Figure FDA0004107673580000102
Figure FDA0004107673580000103
Figure FDA0004107673580000104
Figure FDA0004107673580000105
Figure FDA0004107673580000106
Figure FDA0004107673580000107
Figure FDA0004107673580000108
Figure FDA0004107673580000109
Figure FDA00041076735800001010
wherein ,
Figure FDA00041076735800001011
active power injected into node i provided by the power supply of node k under scenario n, +.>
Figure FDA00041076735800001012
For reactive power injected into node i, p, supplied by the power supply of node k in scenario n i For the active demand of the load of node i, q i Reactive demand for the load of node i, +.>
Figure FDA00041076735800001013
For the maximum active force of the mobile emergency generator car m +.>
Figure FDA00041076735800001014
For the maximum reactive power of the mobile emergency generator car m, < >>
Figure FDA00041076735800001015
For the apparent power capacity of line ij, < > j>
Figure FDA00041076735800001016
To represent the voltage variation of node i associated with a mobile emergency generator connected to node k in scenario n, r ij For the resistance of line ij, x ij Reactance of line ij, +.>
Figure FDA00041076735800001017
For the auxiliary voltage relaxation variable representing node i associated with a mobile emergency generator connected to node k under scenario n, ε is the voltage deviation tolerance, V 0 At rated voltage v ikn As binary variable, 1, y if node i belongs to a micro-grid formed by a mobile emergency power generation car connected at node k in scene n smkn As binary variable, if the emergency power generation car m moving in the scene n moves from the transit position s to k, the emergency power generation car m is 1, V R For reference voltage, +.>
Figure FDA00041076735800001018
A set of children nodes for node i with respect to node k;
the disaster scene constraint of the power distribution network is as follows:
Figure FDA00041076735800001019
wherein ,xijn To represent the binary variable of whether the line ij is closed or not in the scene n, LO n For a set of disaster-stricken lines under scene n, n represents different disaster-stricken scenes, (i, j) represents a line between node i and node j.
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