CN113312761A - Method and system for improving toughness of power distribution network - Google Patents
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Abstract
The invention discloses a method and a system for improving the toughness of a power distribution network, which are used for evaluating the importance of power distribution network components in typhoon weather, establishing an element fault rate model under the action of storm wind, 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 a fault condition when a disaster occurs, and preferentially recovering elements with high importance according to a determined sequence to improve the robustness of the power distribution network; the method comprises the steps of forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disasters and real-time scheduling after disasters, establishing an objective function with 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 cars after the disasters by solving corresponding problems, and completing recovery and promotion of the power distribution network. The invention realizes the improvement of the robustness of the power distribution network; the power distribution network fault recovery time is shortened, and the rapidity of power distribution network recovery is realized.
Description
Technical Field
The invention belongs to the technical field of power system safety planning operation, 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 an electric power system, the defect that the electric power system is insufficient in response capability to extreme disasters is highlighted, especially typhoons have wide influence on the electric power system and strong breaking capacity, power grid equipment is subjected to multiple tripping times and time concentration during typhoon landing and passing, the power grid equipment is mostly in permanent faults, fault equipment is difficult to recover in time and serves as a key link for directly serving users, and severe power failure accidents of a power distribution network under typhoons easily occur. If in 2017, the typhoon 'Tiange' is accumulated to cause that the Guangzhou 10kV line trips 35 times, the overlapping fails 18 times, and 5.6 million households are influenced accumulatively; in 2018, the typhoon "Aiyunni" causes tripping of 61 10kV feeders in Guangzhou region in a short time, wherein 42 feeders are unsuccessfully superposed, and 15.2 ten thousand households are affected accumulatively.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a method and a system for improving the toughness of a power distribution network, a power distribution system is made to rapidly recover a power supply strategy after experiencing an extreme event by comprehensively utilizing various resources, a rapid and efficient power distribution network disaster fault recovery process is realized, and the toughness level of the power distribution system is improved; the resistance and the recovery capability of the power distribution network are improved to the maximum extent, and the loss caused by serious faults due to typhoon disasters is reduced.
The invention adopts the following technical scheme:
a method for improving the toughness of a power distribution network comprises the following steps:
s1, carrying out importance evaluation on the power distribution network components in typhoon weather, carrying out off-line optimization and importance sequencing in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 improve the robustness of the power distribution network;
s2, forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disaster and real-time scheduling after disaster, establishing an objective function with 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 solving corresponding problems to obtain the real-time distribution condition of the mobile emergency power generation cars after disaster, so that the recovery and the promotion of the power distribution network are completed.
Specifically, step S1 specifically includes:
simulating the component fault rate under storm by adopting an exponential fitting method, determining the relation between the fault rate and the fault probability, defining the power sum flowing to a demand node as the system performance, sampling the system state under the storm action by using a non-sequential Monte Carlo simulation method, and determining a target function and model constraint; and obtaining an accumulated distribution function of each component at the repair time by solving the MILP problem configured for each sampling fault, and sequencing the accumulated distribution functions by adopting a Copeland sequencing method.
Further, the failure rate of the assembly under storm:
where w (t) is the wind speed at time t, λwind(w (t)) is the failure rate of the component at wind speed w (t), λnormIs the failure rate of the component under normal conditions; gamma ray1,γ2,γ3The fitting coefficient is obtained by a fitting curve;
the relationship between failure rate and failure probability is:
wherein ,pijIs the failure probability of component (i, j); lambda [ alpha ]ijFailure rate of component (i, j); t isyIs the time associated with the failure rate.
Further, the objective function is:
wherein ,
is normal system performance, FminThe system performance can reach the minimum limit when a disaster happens.
Furthermore, when a disaster happens, the damaged components form a set E 'belonging to E, E is a set of the components, the components in E' are set to be damaged immediately when the disaster happens, a two-state component model is adopted, and the constraint conditions comprise
The component states are constrained as follows:
wherein ,sij(t) is a binary variable, sij(T) E {0,1}, T1, 2, T, which shows the state of component (i, j) E at time T;
the network capacity constraints are as follows:
wherein vertices in the network are divided into three categories: generator node VSTransport node VTAnd a demand node VDContinuous variable fj(t)∈R+Is the power flow received by the demand node j at time t, the continuous variable fij(t)∈R+Is the power flow transmitted from node i to j at time t,
is the transmission capacity, P, of the component (i, j) ∈ Ei SIs a generator node i ∈ VSThe maximum power to be generated is,
for demand node j ∈ VDThe power is transmitted from the generator nodes to all the demand nodes, and the power flow must comply with the physical constraints of the network;
the personnel path constraints are as follows:
wherein, the binary variable xm,n∈[0,1]Indicating whether the repair group is moved from m-component to n-component, if the repair group is moved from m-component to n-component, xm,nTaking 1, otherwise, taking 0; m is a large number, dep denotes warehouse,
indicating the moment of arrival of maintenance personnel at the warehouse, discrete variables
Indicating the moment when the maintenance personnel arrives at the component m; binary variable sm(t) represents the state of component m at time t; binary variable fm,τ∈[0,1]Indicating whether component m is repaired at time t;
indicating the time required for a service person to repair the component;
recording the time f required for a maintenance person to travel from component m to component nm,τTo representWhether component m is repaired at a certain time.
Further, defining the percentile of the cumulative distribution function as the Ω -feature, and obtaining the coppery score of the component m:
wherein ,qk(m) is the kth percentile of CDF for component m repair moment; sm,n,kIs the Copeland score after the kth comparison of m and n,
Smis the coplan fraction of component m, > indicates an advantage.
Specifically, in step S2, an objective function is established with the minimum power outage time of the important load as the target as follows:
wherein α represents the priority weight of the load; p represents the active demand size;
representing the interruption time of the load; beta is aiknIf 0, the load does not recover power and T will be passedinIf the power failure time of (1), the second term
Representing the time required for the load to resume power.
Specifically, in step S2, the constraint conditions that are satisfied include pre-deployment and real-time scheduling constraints, connection relation constraints of network topology, line power flow and power balance constraints, and power distribution network disaster scenario constraints.
Further, the pre-deployment and real-time scheduling constraints are:
τsmikn≤βikn,τsmikn≤xsmkn,τsmikn≥βikn+xsmkn-1
wherein ,τsmiknAuxiliary binary variables, beta, introduced for linearizing the objective functioniknA binary variable, x, representing whether the load of node i is restored by the power supply of node k in scenario nsmknA binary variable c representing real-time dispatching of the mobile emergency generator m from the transfer position s to the node k under the scene nsmA binary variable representing whether the mobile emergency generator m is pre-deployed at the transit location s, CsThe capacity of the mobile emergency power generation vehicles which can be deployed at the transfer position is limited, M represents the mobile emergency power generation vehicles, 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 is a power distribution network node, and G is a candidate node set connected with the mobile emergency power generation vehicles;
the connection relationship constraint of the network topology is as follows:
zkn＝1
vikn≤zkn
vkkn≥zkn
vikn≤vjkn,j＝θk(i)
βikn＝viknlin
wherein ,zknIs a binary variable representing whether node k is a feeder node or a mobile emergency generator connection under scene n, viknTo represent that node i is powered by a power supply connected at node k in scenario n, a binary variable, vkknTo represent the load of node k in scenario n, powered by the power supply to which node k is itself connected, xijnA binary variable, ζ, representing whether line ij is closed or not under scene nk(i, j) is a child node of line ij with respect to node k, [ theta ]k(i) Is a parent node of node i with respect to node k, linThe method comprises the steps that a binary variable representing whether a load switch of a node i is closed or not in a scene n is adopted, h and j represent nodes of a power distribution network, and F is a set formed by all feeder nodes;
the line power flow and power balance constraints are as follows:
wherein ,
for the active power injected into node i provided by the power supply of node k in scenario n,
injecting reactive power, p, into node i provided by the power supply of node k for scene niBeing the active demand of the load of node i, qiBeing the reactive demand of the load of node i,
for the maximum active power output of the mobile emergency generator car m,
the maximum reactive power output of the mobile emergency power generation vehicle m,
for the apparent power capability of the line ij,
to represent the voltage variation of node i associated with a mobile emergency generator car connected to node k in scenario n, rijIs the resistance, x, of line ijijIs the reactance of the line ij and,
to represent the auxiliary voltage relaxation variable for node i associated with a mobile emergency generator car connected to node k in scenario n, ∈ is the voltage deviation tolerance, V0Is a voltage of a rated voltage, and is,
a set of child nodes that are node i with respect to node k;
the power distribution network disaster scene constraints are as follows:
wherein ,xijnA binary variable, LO, representing whether line ij is closed or not in scenario nnIs a set of disaster-affected lines under a scene n, n represents different disaster-affected scenes, and (i, j) represents a line between a node i and a node jAnd (4) a way.
Another technical solution of the present invention is a system for improving toughness of a power distribution network, including:
the robust module is used for evaluating the importance of the power distribution network components in typhoon weather, performing offline optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network;
the recovery module is used for forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disaster and real-time scheduling after disaster, establishing a target function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the target function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation cars after disaster through solving the corresponding problem, and completing the improvement of the rapidity of power distribution network recovery.
Compared with the prior art, the invention has at least the following beneficial effects:
the method for improving the toughness of the power distribution network aims at improving the toughness of the power distribution network, improves the typhoon disaster resistance of a power system by improving the robustness and the rapidity, and realizes a rapid and efficient power distribution network disaster fault recovery process. Firstly, before a disaster, the robustness of a power distribution network is improved by evaluating the importance of components of a power system, and a component failure rate model under the action of a storm is established. According to the model, the system state under the storm effect can be sampled by a non-sequential Monte Carlo simulation method, an optimal repair model is obtained by solving and considering a component maintenance sequence optimization model of unit scheduling aiming at each system state, finally, a distribution function of element repair time can be obtained through sufficient system state sampling, the importance of components is sequenced by a Copeland sequencing method, and compared with the previous research, the model has the advantages that the elasticity and the scheduling of maintenance personnel are considered; according to the identified weak link and the specific situation 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 car, the deployment position of the mobile power generation car is optimized before typhoon occurrence, the power generation car is scheduled to carry out distribution network topology rapid reconfiguration by combining measures such as topology reconfiguration and the like after typhoon occurrence, a microgrid is formed to supply power for important loads, the recovery of the important loads can be effectively guaranteed, the recovery time of distribution network faults is shortened, the rapidity is improved, and the toughness of the distribution network is further improved.
Further, the importance evaluation of the component in step S1 is of great significance for improving disaster resistance, because it plays a crucial role in strengthening the grid structure, designing the recovery strategy, and improving the resource allocation efficiency of disaster prevention and reduction; the results of the component importance assessment may provide us with an effective and economical enhancement strategy to improve the recovery capability of the power system. Common extreme disasters such as storms often cause multiple components to fail simultaneously, and these damaged components cannot be serviced simultaneously due to service personnel and resource constraints. Clearly, the order of repair affects the recovery time, and therefore the importance of the components should be assessed. Component importance assessment can provide an effective repair solution. In component importance assessment, most research has focused on the reliability area considering typical failures, and elasticity-based component importance assessment is rarely considered. Therefore, the patent proposes a component importance assessment method for improving the wind and wave resistance of the power system. The method can provide a component importance ranking method for improving the recovery capability of the power system in advance, so that the optimal recovery strategy can be quickly obtained without long-time calculation when a disaster occurs.
Furthermore, the wind disaster is a natural disaster with relatively high frequency in coastal areas, and the average economic loss caused by power failure due to the wind disaster is very large every year, so that the research of an effective recovery strategy has important meaning on the electric power system under storm so as to improve the recovery capability of the electric power system. To accurately simulate the recovery process, I need to simulate the impact of a storm on the state of the system by combining the storm speed with the failure rate. During storms, trees are more likely to fall on the overhead line and damage the line as tree pressure increases. In addition, friction between the tower and the wind, and between the wire and the wind, can increase, directly causing the tower and the wire to fall or contact other objects. Therefore, wind has great influence on the fault rate of the power transmission line, and the establishment of the fault rate of the components under the storm and the relation between the fault rate and the fault probability can help people to evaluate each component more systematically, so that power supply can be 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 capability. The calculation results of different positions of the maintenance station can provide meaningful planning reference for designers;
further, component state constraints and network capacity constraints place the most fundamental constraints on post-disaster energy balance. After the disaster, the maintenance personnel in the warehouse will come to repair the damaged component, and in order to make the result of the evaluation of the importance of the component more convincing, the travel distance of the maintenance personnel is considered. The restoring force in this patent is related to the entire restoring process, so we cannot consider only the contribution of the components. If we first repair components that contribute significantly to the system but are far away from the warehouse, we may gain less flexibility in the overall process. Thus, the time required for a service person to travel between two damaged components during the repair process is taken into account.
Further, by solving the MILP problem for each sampled fault configuration, the cumulative distribution function for each component repair time may be obtained. In order to rank the importance of components, a Copeland ranking method was introduced. Coplan ranking is a common nonparametric pore-plugging method in the political field. This method does not require much information about the data and runs in a candidate pool where every object has X features. By comparing the objects of different X characteristics in the candidate library in pairs, the scores of all the candidate objects can be calculated, and the candidate objects are sorted according to the scores. This patent employs a modified Copeland method that can be used to rank CDFs. Defining the percentile of the CDF as the X characteristic, so that the Coplanum score (Sm) of the component m can be obtained, and the ranking of the importance of the component is completed;
furthermore, the power distribution system is seriously damaged under extreme disasters, so that large-area power failure of users is caused. The rapid restoration of the power supply is one of the key requirements of a tough power grid. The mobile emergency power generation car is used as a key flexible resource for rapid power supply recovery of a power distribution system, and the utilization efficiency is low at present. By aiming at the minimum of the power failure time of important loads in the 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 can play a greater role after an extreme disaster.
Furthermore, the mobile emergency power generation car is used as a distributed power supply to be connected into a power distribution network to perform topology quick reconstruction, a micro-grid is formed to supply power for load recovery in the region, and therefore the operation constraint of the power distribution network, namely the load flow and power balance constraint, needs to be met. Distribution network topology constraints are required to be met for ensuring a radial distribution network structure. Different disaster scenes can be formed through the constraint of the disaster scenes of the power distribution network, so that the mobile emergency power generation car is pre-deployed in a more targeted manner. The pre-deployment constraint can pre-deploy the mobile emergency power generation cars before a disaster and avoid violating the capacity limit of the transfer station. Real-time scheduling constraints ensure that mobile emergency power-generating cars are scheduled to candidate nodes from a transfer station after a disaster, and meanwhile, in order to improve the utilization rate, the situation that each candidate node is allocated with one mobile emergency power-generating car at most is guaranteed.
In conclusion, the importance evaluation is carried out on the elements before the 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 cars before the disaster and scheduling the mobile emergency power generation cars in real time after the disaster, the fault recovery time of the power distribution network is shortened, and the rapidity of the power distribution network recovery is realized.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a two-dimensional schematic diagram of a power distribution network lift;
FIG. 2 is a network diagram of an abstract topology of an IEEE 14 node system;
FIG. 3 is a graph of the transfer function of five exemplary component restoring torques;
FIG. 4 is a diagram of the results of the Copeland ranking method in an IEEE 14 node system;
FIG. 5 is a traffic information diagram of the test system;
fig. 6 is a power distribution network topology diagram of the test system and a post-disaster microgrid partition result diagram.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It will be understood that the terms "comprises" and/or "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 herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention 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 this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and the relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations, and those 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 the toughness of a power distribution network, where the improvement of the toughness of the power distribution network can be divided into two dimensions, namely, the improvement of the robustness of the power distribution network and the improvement of the rapidity of restoration of the power distribution network; the robustness of the power distribution network is improved, and the scale of faults caused by extreme weather is reduced by evaluating the importance of elements before a disaster; the method has the advantages that the rapidity of power distribution network recovery is improved, and the fault recovery time of the power distribution network is shortened by deploying the mobile emergency power generation cars before the disaster and scheduling the emergency power generation cars in real time after the disaster.
The method for improving the toughness of the power distribution network comprises two aspects, namely, before a disaster, the robustness of the power distribution network is improved by evaluating the importance of power system components, and the fault scale caused by extreme weather is reduced; secondly, the arrangement position of the mobile power generation vehicle is optimized before a disaster by improving the rapidity of the power distribution network, the power generation vehicle is scheduled to form a micro-grid to supply power for important loads by combining measures such as topology reconstruction and the like after the disaster, the recovery time of the fault of the power distribution network is shortened, and the toughness of the power distribution network is further improved. The method specifically comprises the following steps:
s1, importance evaluation is carried out on the power distribution network components in typhoon weather, and robustness of the power distribution network is improved;
the method comprises two stages: the pre-disaster stage obtains the sequencing result in an off-line manner, and the recovery stage obtains the optimal recovery strategy conveniently and quickly; the method comprises the following steps:
performing off-line optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, sampling the state of the wind power generation system by adopting a non-sequential Monte Carlo simulation method, further establishing an optimal recovery strategy model and ranking the importance of the components; when a disaster occurs, failure conditions are acquired, and the elements with high importance are preferentially recovered according to the previously determined sequence.
S101, wind has great influence on the fault rate of the power transmission line, and the component fault rate under the storm is simulated by adopting an exponential fitting method:
where w (t) is the wind speed at time t. Lambda [ alpha ]wind(w (t)) is the failure rate of the component at wind speed w (t), λnormIs the failure rate of the component under normal conditions; gamma ray1,γ2,γ3It is the fitting coefficient obtained from the fitted curve.
Relationship between failure rate and failure probability:
wherein ,pijIs the failure probability of component (i, j); lambda [ alpha ]ijFailure rate of component (i, j); t isyIs the time associated with the failure rate.
S102, sampling the system state under the storm action by a non-sequential Monte Carlo simulation method
Aiming at 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 'epsilon E, and if the components in E' are damaged immediately after the disaster occurs, the system performance reaches the minimum. The goal of the model is to find the repair order of the failed components so that the system achieves maximum recovery capability during the recovery process.
The sum of the power flowing to the demand node is defined as the system performance:
wherein ,fj(t) is the power flow received by demand node j at time t.
To obtain maximum elasticity, the objective function is determined as:
wherein ,
is normal system performance, FminIt is the time of disaster that the system performance reaches the minimum.
In the maximum resilience-based optimal restoration strategy model, the constraints include component states, network capacity, and personnel path constraints.
Component state constraints
A two-state component model is used: sij(t)∈[0,1]T E1, 2, T, which shows the state of component (i, j) E at time T, with the constraint:
equation (5) indicates sij(t) is a binary variable; (6) indicating that once repaired, the component will continue to operate; (7) each component in E' is shown as being damaged at the beginning of the recovery process.
Network capacity constraints
The limitations associated with node and component capacity are:
wherein the continuous variable fj(t)∈R+Is the power flow received by the demand node j at time t; continuous variable fij(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) indicating that the power generated by the generator node cannot exceed the maximum capacity; (9) and (10) is an energy balance constraint; (11) indicating that the actual power provided to the demand node cannot exceed its demand; (12) limiting the power flow transmitted through the component.
Person path constraints
After the disaster, the maintenance personnel at the warehouse will come to repair the damaged component. In order to make the results of the component importance assessment more convincing, the trip distance of the maintenance personnel is taken into account. Assume that the maintenance personnel start from a warehouse. Limitations related to distance and travel time are:
wherein, the binary variable xm,nE (0,1) represents whether the repair group is moved from m-component to n-component, and if the repair group is moved from m-component to n-component, xm,n1, otherwise, 0 is taken; m is a large number; dep represents a warehouse;
indicating the time when the maintenance personnel arrive at the warehouse; discrete variable
Indicating the moment when the maintenance personnel arrives at the component m; binary variable sm(t) represents the state of component m at time t; binary variable fm,t∈[0,1]Indicating whether component m is repaired at time t;
indicating how long it takes for service personnel to repair the component;
record how long it takes for the maintenance person to go from m to n.
Equations (13) and (14) show that the service person can only reach one assembly once and leave once. Equation (15) ensures that the route of the service person is continuous. The maintenance personnel will spend after reaching the m-point
Repair the assembly, they will then cost
Go from m to n. The large M method ensures that routes are continuous, since it is assumed that routes are not continuous, then there must be at least one xm,n0 in turn leads to
At this point, the solution may not be optimal. Equation (16) shows that the maintenance personnel leaves the warehouse at the beginning of the process. Equation (17) indicates that each damaged component can only be repaired once. Equation (18) establishes fm,tAnd
the relationship between; equation (19) shows that the component works well after repair.
The moment when the damaged component m is repaired is denoted as TmCalculated by the following formula:
s103, after the state of the wind power generation system is sampled, an MILP problem configured by each sampling fault is solved, and a cumulative distribution function of each component at the time of repair is obtained.
In order to rank the importance of the components, an improved Copeland ranking method is introduced for ranking the cumulative Distribution function CDF (cumulative Distribution function), defining the percentile number of the CDF as the Ω -feature, thus obtaining the coppan score (Sm) of the component m:
wherein ,qk(m) is the kth percentile of CDF for component m repair moment; sm,n,kIs the Copeland score after the kth comparison of m and n,
Smis the coplan score of component m, the symbol ">" indicates "better" which, in this example, indicates "better" than "; the symbol "<" means "follows".
S2, pre-deploying the mobile emergency power generation cars before a disaster and scheduling in real time after the disaster to form a plurality of micro-grids, so that the fault recovery time of the power distribution network is shortened;
s201, the pre-deployment problem is a scenario-based two-stage random optimization problem, and the pre-deployment decision is determined by a plurality of real-time distribution problems corresponding to the distribution network and road damage under consideration.
In extreme weather, continuous power supply of important loads is guaranteed firstly, so that the minimum power failure time of the important loads in different scenes is taken as a target, and an objective function is as follows:
wherein Ω represents different disaster scenarios; α represents the priority weight of the load; p represents the active demand size;
representing the interruption time of the load; beta is aiknIf 0, the load will not be restored and will experience TinIf the power failure time is 1, the second term in the square brackets
Indicating a load restoration offerThe time required for electricity.
The constraint conditions to be met are: the method comprises the following steps of pre-deploying and real-time scheduling constraints of the mobile emergency power generation car, connection relation constraints of network topology, line power flow and power balance constraints and disaster-affected scene constraints of a power distribution network; finally, an MILP problem is formed through the target function and the constraint condition, 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:
τsmikn≤βikn,τsmikn≤xsmkn,τsmikn≥βikn+xsmkn-1 (23)
connection relation constraint of network topology:
and (3) line power flow and power balance constraint:
and (3) power distribution network disaster scene constraint:
because disaster scenes after disasters are clear, different scenes do not need to be considered in the mobile emergency power generation car real-time scheduling model, and the objective function is provided with the minimum power failure time of the important load as the target:
the constraint conditions to be met can be added with the constraint of the maximum power-off time acceptable by certain key loads, the constraint of the utilization rate of the mobile emergency power-generating car and the like besides the constraint.
And finally, forming an MILP problem through the objective function and the constraint condition, and solving the corresponding problem to obtain the real-time distribution condition of the post-disaster mobile emergency power generation car.
In another embodiment of the present invention, a system for improving the toughness of a power distribution network is provided, where the system can be used to implement the method for improving the toughness of a power distribution network.
The robust module is used for evaluating the importance of the power distribution network components in typhoon weather, performing offline optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network;
the recovery module is used for forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disaster and real-time scheduling after disaster, establishing a target function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the target function and constraint conditions, obtaining the real-time distribution condition of the mobile emergency power generation cars after disaster through solving the corresponding problem, and completing the improvement of the rapidity of power distribution network recovery.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by 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:
the method comprises the steps of evaluating the importance of power distribution network components in typhoon weather, performing offline optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network; the method comprises the steps of forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation car before a disaster and real-time scheduling after the disaster, establishing a target function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the target function and constraint conditions, and solving corresponding problems to obtain the real-time distribution condition of the mobile emergency power generation car after the disaster, so that the rapidity of recovery of the power distribution network is improved.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also 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, one or more instructions, which may be one or more computer programs (including program code), are stored in the storage space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer-readable storage medium to realize the corresponding steps of the method for improving the toughness of the power distribution network in the embodiment; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:
the method comprises the steps of evaluating the importance of power distribution network components in typhoon weather, performing offline optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network; the method comprises the steps of forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation car before a disaster and real-time scheduling after the disaster, establishing a target function by taking the minimum power failure time of important loads in different scenes as a target, forming an MILP problem through the target function and constraint conditions, and solving corresponding problems to obtain the real-time distribution condition of the mobile emergency power generation car after the disaster, so that the rapidity of recovery of the power distribution network is improved.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. 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 present invention, presented in the figures, 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 derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
The present invention uses the IEEE-14 node algorithm for component importance evaluation, and an IEEE-14 node system comprising 14 nodes and 20 lines is converted into a topological network consisting of nodes and edges, as shown in FIG. 2. The nodes are divided into three types, generator nodes, demand nodes and transmission nodes. The strategy and time of travel of the service personnel is affected by the distance between the components, and the entire IEEE 14 bus network is divided into six zones. The distance between two adjacent areas is defined as a distance unit.
TABLE 1 initial parameters of the IEEE 14 bus system
After several simulations (in this case 1000), CDFs of the repair moments for 20 parts can be obtained. Fig. 3 shows the CDFs at the time of repair for five representative components. It can be seen that the repair time for component <6, 11> (the 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 the system will obtain a greater value of resiliency as component <6, 11> is repaired earlier than component <4, 7 >.
However, the relative importance of not all components may be so intuitively determined. For example, the importance relationship between component <6, 12> and component <6, 13> is difficult to judge because their distribution functions intersect.
Thus, the importance of such components can be ranked using the coplan 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:
-components connecting two regions, such as <4, 9>, <5, 6>, <7, 9 >;
demand nodes and generator nodes close to the warehouse, e.g. <3, 4>, <6, 11 >.
There are two types of components that score lower:
components between generator nodes, e.g. <1, 2>, <2, 3 >;
and a plurality of components are arranged between the two nodes. Then these components have lower scores, e.g. <2, 4> <2, 5 >. Components with high copperand scores should have higher priority in the recovery process, which makes the overall recovery process more efficient.
Fig. 5 and 6 are test systems for improving the recovery rapidity of the post-disaster distribution network according to the invention. Fig. 5 shows a traffic geographic information map including 51 intersections, 82 edges, and 3 transfer stations S1, S2, S3. Fig. 6 shows a power distribution system with 114 nodes table 2 lists the available capacity of mobile emergency power cars.
TABLE 2 available capacities of MEGs
The pre-deployment case is shown in the second column of table 3.
TABLE 3 MEGs Pre-deployment and real-time scheduling results
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 zoning situation of the microgrid is shown by shading in fig. 6. For example, MEG1 is pre-deployed at the transfer station S2, and is scheduled to node 403 in real time after disaster, so as to form a micro grid to restore power to the loads connected to nodes 403 and 404.
In summary, the method and the system for improving the toughness of the power distribution network not only consider the elasticity, but also consider the scheduling of maintenance personnel. In each simulation process, an MILP is solved, and a cumulative distribution function of the repair moment of each component is obtained through multiple simulation processes. The components were then ranked using the Copeland ranking method. Once the component grade is obtained, in the event of a disaster, the system operator may schedule maintenance personnel to perform maintenance on the damaged component according to the grade order. Those components with higher ranking scores will be repaired earlier. The strategies can be implemented on line, so that the recovery speed of the power grid after a disaster occurs is greatly increased; many users often experience long term power outages due to continued damage to the power distribution grid components after an extreme disaster. Mobile emergency power cars are a flexible resource to quickly restore power supply. The mobile emergency power generation vehicle is used as a distributed power supply to be scheduled, and the power distribution network topology is reconstructed into a plurality of micro power grids to recover load power supply. The method is realized by a two-stage scheduling framework of pre-deployment and real-time allocation. The examples demonstrate the effectiveness of the proposed method. The scheduling method of the mobile emergency power generation car can reduce large-scale power failure after the extreme disaster occurs, and can be better used for toughness emergency response to the extreme disaster.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. A method for improving the toughness of a power distribution network is characterized by comprising the following steps:
s1, carrying out importance evaluation on the power distribution network components in typhoon weather, carrying out off-line optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network;
s2, forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disaster and real-time scheduling after disaster, establishing an objective function with 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 solving corresponding problems to obtain the real-time distribution condition of the mobile emergency power generation cars after disaster, so that the recovery and the promotion of the power distribution network are completed.
2. The method according to claim 1, wherein step S1 is specifically:
simulating the component fault rate under storm by adopting an exponential fitting method, determining the relation between the fault rate and the fault probability, defining the power sum flowing to a demand node as the system performance, sampling the system state under the storm action by using a non-sequential Monte Carlo simulation method, and determining a target function and model constraint; and obtaining an accumulated distribution function of each component at the repair time by solving the MILP problem configured for each sampling fault, and sequencing the accumulated distribution functions by adopting a Copeland sequencing method.
3. The method of claim 2, wherein the component failure rate under storm wind:
where w (t) is the wind speed at time t, λwind(w (t)) is the failure rate of the component at wind speed w (t), λnormIs the failure rate of the component under normal conditions; gamma ray1,γ2,γ3The fitting coefficient is obtained by a fitting curve;
the relationship between failure rate and failure probability is:
wherein ,pijIs the failure probability of component (i, j); lambda [ alpha ]ijFailure rate of component (i, j); t isyIs the time associated with the failure rate.
4. The method of claim 2, wherein the objective function is:
wherein ,
is normal system performance, FminThe system performance can reach the minimum limit when a disaster happens.
5. The method of claim 2, wherein in case of disaster, the damaged components form a set E 'E, E is a set of components, and assuming that the components in E' are damaged immediately in case of disaster, a two-state component model is used, and the constraint conditions include
The component states are constrained as follows:
wherein ,sij(t) is a binary variable, sij(T) E {0,1}, T1, 2, T, which shows the state of component (i, j) E at time T;
the network capacity constraints are as follows:
wherein vertices in the network are divided into three categories: generator node VSTransport node VTAnd a demand node VDContinuous variable fj(t)∈R+Is the power flow received by the demand node j at time t, the continuous variable fij(t)∈R+Is the power flow transmitted from node i to j at time t,
is the transmission capacity, P, of the component (i, j) ∈ Ei SIs a generator node i ∈ VSThe maximum power to be generated is,
for demand node j ∈ VDThe power is transmitted from the generator nodes to all the demand nodes, and the power flow must comply with the physical constraints of the network;
the personnel path constraints are as follows:
wherein, the binary variable xm,n∈[0,1]Indicating whether the repair group is moved from m-component to n-component, x if the repair group is moved from m-component to n-componentm,nTaking 1, otherwise, taking 0; m is a large number, dep denotes warehouse,
indicating the moment of arrival of maintenance personnel at the warehouse, discrete variables
Indicating the moment when the maintenance personnel arrives at the component m; binary variable sm(t) represents the state of component m at time t; binary variable fm,τ∈[0,1]Indicating whether component m is repaired at time t;
indicating the time required for a service person to repair the component;
recording the time f required for a service person to travel from component m to component nm,τIndicating whether the component m is repaired at a certain time.
6. Method according to claim 2, characterized in that defining the percentile of the cumulative distribution function as the Ω -feature, the coplan score of component m is obtained:
wherein ,qk(m) is the kth percentile of CDF for component m repair moment; sm,n,kIs the Copeland score after the kth comparison of m and n,
Smis the coplan fraction of component m, > indicates an advantage.
7. The method according to claim 1, wherein in step S2, the objective function is established with the objective of minimum outage time of the important load as follows:
wherein α represents the priority weight of the load; p represents the active demand size;
representing the interruption time of the load; beta is aiknIf 0, the load does not recover power and T will be passedinIf the power failure time of (1), the second term
Representing the time required for the load to resume power.
8. The method according to claim 1, wherein the constraint conditions satisfied in step S2 include pre-deployment and real-time scheduling constraints, connection relation constraints of network topology, line power flow and power balance constraints, and distribution network disaster scenario constraints.
9. The method of claim 8, wherein the pre-deployment and real-time scheduling constraints are:
τsmikn≤βikn,τsmikn≤xsmkn,τsmikn≥βikn+xsmkn-1
wherein ,τsmiknAuxiliary binary variables, beta, introduced for linearizing the objective functioniknA binary variable, x, representing whether the load of node i is restored by the power supply of node k in scenario nsmknA binary variable c representing real-time dispatching of the mobile emergency generator m from the transfer position s to the node k in the scene nsmA binary variable representing whether the mobile emergency generator m is pre-deployed at the transit location s, CsThe capacity of the mobile emergency power generation vehicles which can be deployed at the transfer position is limited, M represents the mobile emergency power generation vehicles, 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 is a power distribution network node, and G is a candidate node set connected with the mobile emergency power generation vehicles;
the connection relationship constraint of the network topology is as follows:
zkn＝1
vikn≤zkn
vkkn≥zkn
vikn≤vjkn,j＝θk(i)
βikn＝viknlin
wherein ,zknIs a binary variable representing whether node k is a feeder node or a mobile emergency generator connection under scene n, viknTo represent that node i is powered by a power supply connected at node k in scenario n, a binary variable, vkknTo represent the load of node k in scenario n, powered by the power supply to which node k is itself connected, xijnTo represent a binary variable, ζ, whether line ij is closed or not in scenario nk(i, j) is a child node of line ij with respect to node k, [ theta ]k(i) Is a parent node of node i with respect to node k, linThe method comprises the following steps that a binary variable representing whether a load switch of a node i is closed or not in a scene n is adopted, h, j represent nodes of a power distribution network, and F is a set formed by all feeder nodes;
the line power flow and power balance constraints are as follows:
wherein ,
for the active power injected into node i provided by the power supply of node k in scenario n,
injecting reactive power, p, into node i provided by the power supply of node k in scenario niBeing the active demand of the load of node i, qiBeing the reactive demand of the load of node i,
for the maximum active power output of the mobile emergency generator car m,
the maximum reactive power output of the mobile emergency power generation vehicle m,
for the apparent power capability of the line ij,
to represent the voltage variation of node i associated with a mobile emergency generator car connected to node k in scenario n, rijIs the resistance, x, of line ijijIs the reactance of the line ij and,
to represent the auxiliary voltage relaxation variable for node i associated with a mobile emergency generator car connected to node k in scenario n, ∈ is the voltage deviation tolerance, V0Is a voltage of a rated voltage, and is,
a set of child nodes that are node i with respect to node k;
the power distribution network disaster scene constraints are as follows:
wherein ,xijnA binary variable, LO, representing whether line ij is closed or not in scenario nnThe set of the disaster-affected lines under the scene n is shown, n represents different disaster-affected scenes, and (i, j) represents lines between the node i and the node j.
10. A system for improving toughness of a power distribution network, comprising:
the robust module is used for evaluating the importance of the power distribution network components in typhoon weather, performing offline optimization and importance ranking in the pre-disaster stage, establishing an element fault rate model under the action of storm wind, 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 ranking 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 improve the robustness of the power distribution network;
the recovery module is used for forming a plurality of micro-grids through pre-deployment of the mobile emergency power generation cars before disasters and real-time scheduling after disasters, establishing an objective function with 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 cars after disasters by solving the corresponding problem, and completing the improvement of the rapidity of power distribution network recovery.
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