Power distribution network operation state determination method and device, electronic equipment and storage medium

Abstract

The application provides a power distribution network operation state determination method and device, electronic equipment and storage medium, the method comprises the steps of: acquiring environment data, power distribution network fault model and current operation state of power distribution network; according to environment data and power distribution network fault model, the fault conditions corresponding to multiple nodes and multiple branches at multiple times are determined respectively; based on the current operation state of the power distribution network, the fault conditions corresponding to multiple nodes and multiple branches at multiple times respectively, and the corresponding condition constraint, a power distribution network elasticity improvement model is constructed, the maximum value of the sum of the load recovery degree in multiple times is solved, and the minimum value of the switch state switching frequency in multiple times; the operation state of the power distribution network in the preset time period is determined by solving the power distribution network elasticity improvement model. Based on this, a multi-objective long-time dynamic power distribution network reconstruction scheme for improving the elasticity of the power distribution network can be obtained, which can greatly improve the load recovery amount of the power distribution network after a period of disaster evolution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation, and in particular relates to a power distribution network operation state determination method and device, an electronic device and a storage medium. BACKGROUND

[0002] Modern society highly depends on stable and reliable power supply for normal operation, and the ability of the power system to prevent, resist extreme events and quickly restore load is called power system resilience. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change points out that global climate change is intensifying, the risk of extreme weather disasters such as typhoons and heavy rains is rapidly increasing, and in addition to the special terrain environment of some cities and insufficient flood drainage capacity, waterlogging disasters occur frequently. Waterlogging disasters have the characteristics of strong uneven uncertainty, great damage to circuit components, and difficulty in repairing components due to water accumulation, which pose a serious threat to the power system. Therefore, how to improve the resilience of the power distribution network and dynamically improve it is a technical problem that needs to be solved in the field in view of flood disasters. SUMMARY

[0003] The present application provides a power distribution network operation state determination method and device, an electronic device and a storage medium, by proposing a multi-objective long-time dynamic power distribution network reconstruction scheme to improve the resilience of the power distribution network, which can greatly improve the load recovery amount of the power distribution network after a period of disaster evolution, especially suitable for disaster scenarios such as flood disasters that have long duration and complex effects on the power distribution system, and improves the resilience of the power distribution network to flood disasters.

[0004] In a first aspect, the present application provides a power distribution network operation state determination method, which comprises:

[0005] Obtaining environment data, a power distribution network fault model and a current operation state of the power distribution network, the environment data comprising wind speed and water depth corresponding to a plurality of time points in a preset time period respectively, the power distribution network fault model being used to predict fault probabilities of a plurality of nodes and a plurality of branches included in the power distribution network, and the current operation state of the power distribution network comprising load power of the plurality of nodes and current switch states of the plurality of branches;

[0006] According to the environment data and the power distribution network fault model, determining fault probabilities of the plurality of nodes and the plurality of branches corresponding to a plurality of time points respectively;

[0007] According to the fault probabilities of the plurality of nodes and the plurality of branches corresponding to a plurality of time points respectively, determining fault conditions of the plurality of nodes and the plurality of branches corresponding to a plurality of time points respectively;

[0008] The power distribution network elasticity improvement model is constructed based on a current operation state of the power distribution network, fault conditions of multiple nodes and multiple branches corresponding to multiple time points, and target condition constraints. A solution target of the power distribution network elasticity improvement model includes a maximum value of a sum of load recovery degrees in multiple time points and a minimum value of switching times of switch states in multiple time points. The target condition constraints include power flow balance constraints.

[0009] The operation state of the power distribution network in a preset time period is determined based on a solution result of the power distribution network elasticity improvement model.

[0010] It can be seen that, in the present application, the fault conditions of multiple nodes and multiple branches in a time period are determined according to wind speed and water depth in the time period and a power distribution network fault model. Then, a power distribution network elasticity improvement model is constructed according to the fault conditions of the multiple nodes and the multiple branches in the time period, a current operation state of the power distribution network, and corresponding target condition constraints. The power distribution network elasticity improvement model is solved with a maximum value of a sum of load recovery degrees in multiple time points and a minimum value of switching times of switch states in multiple time points as a target. The operation state of the power distribution network in the time period is obtained. In this way, dynamic reconstruction of the power distribution network can be realized based on the operation state of the power distribution network in the time period. A method for quantifying power grid failure probability in a wind and waterlogging scenario is provided. A multi-objective long-time dynamic power distribution network reconstruction scheme for improving the elasticity of the power distribution network is proposed. The load recovery amount of the power distribution network after a disaster evolves for a period of time can be greatly improved. The method is especially suitable for disaster scenarios such as wind and waterlogging disasters, which have a long duration and complex effects on the power distribution system. The elasticity of the power distribution network against waterlogging and water immersion disasters is improved.

[0011] In a feasible example, the fault probability corresponding to multiple time points of multiple nodes and multiple branches is determined, including: when the target parameter is less than the first threshold value, the fault probability is the target probability, the target parameter is the wind speed or the water depth, and the target probability is the fault probability when the node or the branch is not affected by the target parameter; when the target parameter is not less than the first threshold value and less than the second threshold value, the fault probability is determined according to the distribution function of the first value, the first value is determined according to the ratio of the first parameter and the second parameter, the second parameter is the standard deviation of the natural logarithm of the target parameter causing the fault, and the first parameter is the logarithm of the third parameter, the third parameter is the ratio between the target parameter and the median of the target parameter at the time of the fault; when the target parameter is not less than the second threshold value, the fault probability is one.

[0012] In a feasible example, the power distribution network fault model includes a tower fault model, an overhead line fault model, and a substation fault model. The tower fault model and the overhead line fault model determine the fault probability according to the wind speed. The substation fault model determines the fault probability according to the water depth.

[0013] In the present application, the corresponding fault models are respectively established based on the towers, overhead lines and substations, so that the equipment of different distribution networks can be adapted, and the accuracy of the distribution network fault model is improved.

[0014] In a feasible example, the fault conditions of the plurality of nodes and the plurality of branches at the plurality of time points are determined according to the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of time points, comprising: for each time point in the plurality of time points, generating a plurality of first uniform random numbers corresponding to the plurality of nodes respectively, and a plurality of second uniform random numbers corresponding to the plurality of branches respectively, the first uniform random number and the second uniform random number being between zero and one; when the first uniform random number corresponding to each node in the plurality of nodes is not greater than the fault probability corresponding to each node at each time point, the fault condition of each node is determined as fault; when the first uniform random number corresponding to each node in the plurality of nodes is greater than the fault probability corresponding to each node at each time point, the fault condition of each node is determined as non-fault; when the second uniform random number corresponding to each branch in the plurality of branches is not greater than the fault probability corresponding to each branch at each time point, the fault condition of each branch is determined as fault; when the second uniform random number corresponding to each branch in the plurality of branches is greater than the fault probability corresponding to each branch at each time point, the fault condition of each branch is determined as non-fault.

[0015] In the present application, the fault scenarios are simulated by the foregoing method, various possible fault combinations can be covered, and thus the subsequent implementation of the resilience improvement of the distribution network is more beneficial.

[0016] In a feasible example, the distribution network resilience improvement model is represented as:

[0017]

[0018] Wherein, α and β are used to represent the weight values, and the sum of α and β is 1; ω k is used to represent the weight value corresponding to node k in the plurality of nodes; y k,t is used to represent the fault condition corresponding to node k at t time point, and y k,t is a 0-1 variable, which is 1 when the node is faulted, and 0 when the node is not faulted; P k,t is used to represent the load power size corresponding to node k at t time point; T is used to represent a preset time period, B is used to represent a set of the plurality of nodes, and E is used to represent a set of the plurality of branches; a l,t is used to represent the fault condition corresponding to branch l at t time point, a l,t-1 is used to represent the fault condition corresponding to branch l at t-1 time point, and similarly a l,t is a 0-1 variable, which is 1 when the branch is faulted, and 0 when the branch is not faulted.

[0019] In the present application, based on the foregoing power distribution network resilience promotion model, a power distribution network reconfiguration scheme capable of greatly improving the load recovery amount of the power distribution network after a certain period of disaster evolution can be obtained.

[0020] In a feasible example, the current operating state of the power distribution network further includes nodes in which distributed power sources are deployed in the plurality of nodes, and the target condition constraint further includes an output power constraint of the distributed power source, which is used to constrain the output power of the distributed power source to be within a preset threshold.

[0021] In a feasible example, the target condition constraint further includes a closed branch constraint and a single commodity flow constraint; the closed branch constraint is used to constrain the number of closed branches in the plurality of branches to be equal to the number of nodes minus the number of root nodes in the plurality of nodes; the single commodity flow constraint includes a flow constraint, a root node number constraint, and a branch constraint; the flow constraint is used to constrain the sum of the inflow power of the non-root nodes in the plurality of nodes to be equal to the sum of the outflow power plus the load of the node, and / or the sum of the inflow power of the root nodes in the plurality of nodes to be less than the sum of the outflow power plus the load of the node; the root node number constraint is used to constrain the number of root nodes in the plurality of nodes to be less than the number of failed branches in the plurality of branches plus one; and the branch constraint is used to constrain the flow power of the failed branches in the plurality of branches to be zero.

[0022] In a second aspect, the present application provides a power distribution network operating state determination device, which comprises:

[0023] An acquisition unit is configured to acquire environmental data, a power distribution network failure model, and a current operating state of the power distribution network, wherein the environmental data includes wind speeds and water depths corresponding to a plurality of time points in a preset time period, the power distribution network failure model is used to predict failure probabilities of a plurality of nodes and a plurality of branches included in the power distribution network, and the current operating state of the power distribution network includes load powers of the plurality of nodes and current switch states of the plurality of branches.

[0024] A processing unit is configured to determine failure probabilities of the plurality of nodes and the plurality of branches corresponding to the plurality of time points, according to the environmental data and the power distribution network failure model.

[0025] The processing unit is further configured to determine failure conditions of the plurality of nodes and the plurality of branches corresponding to the plurality of time points, according to the failure probabilities of the plurality of nodes and the plurality of branches corresponding to the plurality of time points.

[0026] The processing unit is further configured to construct a power distribution network resilience promotion model based on the current operating state of the power distribution network, the failure conditions of the plurality of nodes and the plurality of branches corresponding to the plurality of time points, and target condition constraints, wherein a solution target of the power distribution network resilience promotion model includes a maximum value of a sum of load recovery degrees in the plurality of time points and a minimum value of a number of switch state switching times in the plurality of time points, and the target condition constraints include a power flow balance constraint.

[0027] The processing unit is further configured to solve the power distribution network elasticity promotion model, and determine an operation state of the power distribution network in a preset time period based on a solution result.

[0028] In a third aspect, the present application provides an electronic device, which comprises a processor, a memory and a communication interface, the processor, the memory and the communication interface are connected to each other and complete communication work with each other, the memory stores executable program codes, the communication interface is used for wireless communication, and the processor is used for calling the executable program codes stored in the memory and executing part or all of the steps described in the method of any one of the first aspect.

[0029] In a fourth aspect, the present application provides a computer readable storage medium, which stores electronic data, and the electronic data is used for executing the electronic data to realize part or all of the steps described in the first aspect of the present application when executed by a processor.

[0030] In a fifth aspect, the present application provides a computer program product, which comprises a computer program operable to cause a computer to execute part or all of the steps described in the first aspect of the present application. The computer program product can be a software installation package. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0032] Figure 1 A structural schematic diagram of a power distribution network elasticity promotion system provided by an embodiment of the present application;

[0033] Figure 2 A flowchart of a power distribution network operation state determination method provided by an embodiment of the present application;

[0034] Figure 3 A schematic diagram of a tower and line vulnerability curve to wind speed provided by an embodiment of the present application;

[0035] Figure 4 A schematic diagram of a substation damage curve when the substation is flooded provided by an embodiment of the present application;

[0036] Figure 5 A schematic diagram of a substation flooding vulnerability curve provided by an embodiment of the present application;

[0037] Figure 6A substation waterlogging vulnerability curve schematic diagram provided for an embodiment of the present application;

[0038] Figure 7 A functional unit component block diagram of a power distribution network operation state determination device provided for an embodiment of the present application;

[0039] Figure 8 A functional unit component block diagram of another power distribution network operation state determination device provided for an embodiment of the present application;

[0040] Figure 9 A structural block diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present application.

[0042] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps is not limited to the listed steps, but can optionally include steps not listed or can optionally include other steps inherent to the process, method, product or device.

[0043] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It will be explicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0044] Please refer to Figure 1 , Figure 1 A structural schematic diagram of a power distribution network resilience improvement system provided for an embodiment of the present application, as shown in Figure 1 The power distribution network resilience improvement system 100 includes a server 101 and a power distribution network 102.

[0045] The server 101 is a server, a server cluster, a cloud server, a cloud computing service center, or other forms of devices with computing capabilities.

[0046] The power distribution network 102 includes towers, overhead lines, substations, and buried cables, etc. The server 101 is configured to acquire devices in the power distribution network 102, and divide the devices in the power distribution network 102 into a plurality of nodes and a plurality of branches based on a radial topology of the power distribution network.

[0047] Specifically, the server 101 acquires environment data, a power distribution network fault model, and a current operating state of the power distribution network, the environment data including wind speeds and water depths corresponding to a plurality of time points in a preset time period respectively, the power distribution network fault model being used to predict fault probabilities of a plurality of nodes and a plurality of branches included in the power distribution network 102, and the current operating state of the power distribution network including load powers of the plurality of nodes and current switch states of the plurality of branches; determines, according to the environment data and the power distribution network fault model, fault probabilities of the plurality of nodes and the plurality of branches corresponding to the plurality of time points respectively; determines, according to the fault probabilities of the plurality of nodes and the plurality of branches corresponding to the plurality of time points respectively, fault conditions of the plurality of nodes and the plurality of branches corresponding to the plurality of time points respectively; constructs, based on the current operating state of the power distribution network, the fault conditions of the plurality of nodes and the plurality of branches corresponding to the plurality of time points respectively, and a target conditional constraint, a power distribution network resilience improvement model, a solving target of the power distribution network resilience improvement model including a maximum value of a sum of load recovery degrees in the plurality of time points and a minimum value of switch state switching times in the plurality of time points; the target conditional constraint includes a power flow balance constraint; and finally solves the power distribution network resilience improvement model, and determines, based on a solving result, an operating state of the power distribution network in the preset time period. Subsequently, the plurality of nodes and the plurality of branches included in the power distribution network can be configured based on the operating state of the power distribution network in the preset time period, so that the load recovery amount of the power distribution network after a disaster evolves for a period of time can be greatly improved, the service life of the branch switch is improved, and the resilience of the power distribution network to waterlogging and flooding disasters is improved.

[0048] Based on this, the embodiment of the present application provides a power distribution network operating state determination method, which will be described in detail below in combination with the accompanying drawings.

[0049] Please refer to Figure 2 , Figure 2 A flowchart of a power distribution network operating state determination method provided by the embodiment of the present application is shown in the figure, and the method is applied to the above-mentioned server, as shown in Figure 2 the figure, the method includes the following steps:

[0050] Step S201, environment data, a power distribution network fault model, and a current operating state of the power distribution network are acquired.

[0051] The environment data includes wind speeds and water depths corresponding to a plurality of time points in a preset time period respectively, the power distribution network fault model is used to predict fault probabilities of a plurality of nodes and a plurality of branches included in the power distribution network 102, and the current operating state of the power distribution network includes load powers of the plurality of nodes and current switch states of the plurality of branches.

[0052] Wherein, the power distribution network failure model is mainly obtained by modeling the influence of typhoon and waterlogging scenarios on the power system. Typhoon, heavy rain and flood can further cause waterlogging. Urban waterlogging refers to the phenomenon that the underground space or low-lying area is flooded due to the precipitation far exceeding the interception and drainage capacity of an area. The substation flooding caused by urban waterlogging and the damage of distribution network lines and towers caused by typhoon are the main failure modes of the power grid in typhoon weather. After the weather parameters (such as wind speed, precipitation depth) can be obtained from meteorological bureau or other database sources, the vulnerability curves of vulnerable elements such as distribution network lines, towers, substations and buried cables under waterlogging and typhoon scenarios can be established. The failure probability of power system elements can be obtained by combining the vulnerability curve with the weather conditions, and then the disaster scenario can be generated, and the targeted power distribution network resilience improvement strategy is given. This part firstly models the vulnerability curves of each element in the power distribution network under the wind and water disasters.

[0053] The fragility function describes the probability of failure of an element or component of an element in the power distribution network, which depends on the load related to the intensity of the potential hazard (such as substation waterlogging depth, typhoon wind speed, etc.). The establishment of the vulnerability curve is based on: a) statistical analysis of a large amount of historical data; b) experiment; c) model analysis; d) professional judgment. This method combines historical data in open source data sets, combines existing vulnerability curve models, and establishes vulnerability curves for each element in the power distribution network to determine the failure probability of each element in the power distribution network under a specific disaster scenario.

[0054] The strength characteristics of various engineering structures are lognormal distribution. For a given device, the risk factor strength S d (depth of immersion) can be described by a lognormal distribution, that is:

[0055] wherein, is the median of the risk threshold (the depth of immersion at the time of damage), β ds is the standard deviation of the natural logarithm of the device damage threshold. Then the probability POS (Probability of an OutageStart) of the device damage at this risk intensity can be represented as:

[0056] The following discusses the power distribution network tower, power distribution network line and substation respectively.

[0057] Specifically, in a feasible embodiment, the power distribution network failure model includes a tower failure model, an overhead line failure model and a substation failure model; the tower failure model and the overhead line failure model determine the failure probability according to the wind speed; the substation failure model determines the failure probability according to the water depth.

[0058] It can be understood that in the present application, the tower and the substation can correspond to the node in the power distribution network, and the overhead line can correspond to the branch in the power distribution network, or the tower and the overhead line can correspond to the branch in the power distribution network.

[0059] Meanwhile, in a feasible embodiment, the determination of the fault probability corresponding to the plurality of nodes and the plurality of branches at the plurality of time points comprises: when the target parameter is less than a first threshold value, the fault probability is a target probability, the target parameter is the wind speed or the water depth, and the target probability is the fault probability of the node or the branch not affected by the target parameter; when the target parameter is not less than the first threshold value and less than a second threshold value, the fault probability is determined according to a distribution function of a first value, the first value is determined according to a ratio of a first parameter and a second parameter, the second parameter is a standard deviation of a natural logarithm of the target parameter causing the fault, and the first parameter is a logarithm of a third parameter, the third parameter being a ratio between the target parameter and a median of the target parameter at the time of the fault; and when the target parameter is not less than the second threshold value, the fault probability is one.

[0060] The tower failure model, the overhead line failure model and the substation failure model will be described below.

[0061] For the tower failure model:

[0062] It is assumed that the weather condition is good (i.e. the wind speed is low), and the tower of the power distribution network will not be damaged. The vulnerability curve of the tower of the power distribution network is constructed as follows, and the probability of a certain tower of the power distribution network being in a fault state at time T can be simplified and described as a function of the wind speed passing through the tower, which can be expressed as:

[0063] wherein v is the wind speed passing through the tower, v critical represents the wind speed at which the probability of the tower failure is about 1, and v collapse is the wind speed at which the probability of the tower not being damaged can be ignored. P T,Tow,hv is the failure probability of the tower when the wind speed is high, which can be expressed as: This can be linearized here, wherein β and depend on the tower condition, and β here is expressed as the standard deviation of the natural logarithm of the wind speed causing the tower failure obtained by statistical analysis; is expressed as the median of the wind speed causing the tower failure obtained by statistical analysis. Wherein Φ is the distribution function of the standard normal distribution. Exemplarily, if it is considered here that all tower parameters are the same, it can be obtained that β≈0.3268,

[0064] For the overhead line failure model:

[0065] The line can also be affected by stormy weather. It is assumed that the failure of the line is independent of the failure of the tower, and thus its vulnerability curve is different. The vulnerability of the line should also be related to the wind speed, and the function corresponding to the overhead line failure model can be expressed as:

[0066] wherein, is the failure rate of the line in "good weather" (when the wind speed is low). It can be understood that, since the wind speed in the area through which the line passes can be different, but it is assumed that the line breakage is only related to the maximum wind speed borne by the line, the wind speed v here is considered to be the highest wind speed in the area through which the line passes, v critical represents the wind speed at which the probability of failure of the line is about 1, v collapse represents the wind speed at which the probability of failure of the line based on the wind speed can be ignored.

[0067] wherein, P T,Line,hv is the failure probability of the line when the wind speed is high but still within the bearing range of the line, which can be further simplified as:

[0068] which is also linearized here, wherein β and are also dependent on the line condition, and β here is expressed as the standard deviation of the natural logarithm of the wind speed causing the failure of the line obtained by statistical analysis; is expressed as the median of the wind speed causing the failure of the line obtained by statistical analysis. For example, if it is assumed here that the parameters of all lines are the same, it can be obtained that β ≈ 0.1926,

[0069] For example, please refer to Figure 3 , Figure 3 is a schematic diagram of the vulnerability curves of the towers and lines to wind speed provided by the embodiments of the present application. As shown in Figure 3 , the horizontal axis is the wind speed, the vertical axis is the failure probability, and the left side of the dotted line and solid line images respectively are the linearization results of the line to the wind speed and the original lognormal distribution curve, and the right side of the dotted line and solid line images respectively are the linearization results of the tower to the wind speed and the original lognormal distribution curve.

[0070] And for a certain time T, it can be assumed that the failure of the tower and the line is independent of each other, and the probability of failure of the branch e due to too high wind speed can be expressed as: wherein P T,Tow and P T,Line are respectively the failure probability of a single tower or line at time T, which is obtained by mapping the (maximum) wind speed at the location of the tower (line) to the vulnerability curve of the tower (line), and T and L respectively represent the set of towers and lines in the branch e.

[0071] For the substation failure model:

[0072] When the storm further develops and urban waterlogging occurs, the outdoor switch cabinet, circuit breaker equipment, transformer and buried cable of the ground substation may be flooded, further affecting the power system. Since the failure conditions of such equipment are all related to the water depth of the location, their vulnerability models are similar, and only some parameters differ, this paper takes the substation as an example to establish the model.

[0073] As shown in Figure 4 , a damage curve diagram of a substation when it is flooded by water is provided for the embodiments of the present application, as shown in Figure 4 , the horizontal axis is water depth, and the vertical axis is damage ratio. When the water depth is D, the damage percentage u of the substation can be expressed as: u = (4.68D + 0.77) %. Figure 4 When the substation is regarded as a whole, when the devices in the substation are flooded and the damaged components exceed 3%, the substation stops serving and the node trips. The water level depth D can be used as the risk threshold for the substation to stop serving. As shown in

[0074] , a vulnerability curve diagram of a substation water flooding is provided for the embodiments of the present application, as shown in Figure 5 , from left to right are the softened vulnerability curve, the original vulnerability curve and the sharpened vulnerability curve, the horizontal axis is water depth, and the vertical axis is failure probability. It should be pointed out that different regions may have different design standards and protection measures, and the parameters such as substation topology are also different, but the curve form can be used as a reference. Figure 5 In the substation flooding failure model established in this paper, the failure probability P T,Sub of a substation at time T can be simplified as a piecewise linear function of the substation water depth D:

[0075] where D is the water depth of the substation, D collapse is the water depth at which the failure probability of the substation can be ignored (here it is 0.52m, which can be reconstructed according to the historical data of the research object to adapt to the actual situation). Similarly, the substation damage probability P T,Sub,hD (D) when the water level is high can be simplified as:

[0076] As shown in Figure 6 , a vulnerability curve diagram of a substation waterlogging is provided for the embodiments of the present application. As shown in Figure 6 , it includes the waterlogging vulnerability curve of a single substation, the horizontal axis is water depth, and the vertical axis is failure probability. The solid line corresponds to the lognormal distribution function, and the dotted line corresponds to the linearized result. Figure 6 ​​

[0077] It can be understood that after more historical data of the failure of power system components and real-time weather conditions are obtained subsequently, the existing model can also be corrected, thereby establishing a more accurate vulnerability curve and improving the confidence of the model.

[0078] In the present application, corresponding fault models are established based on towers, overhead lines and substations, thereby adapting to different equipment of the distribution network and improving the accuracy of the distribution network fault model.

[0079] Step S202, according to the environmental data and the distribution network fault model, determining the fault probability of each node and branch corresponding to each time.

[0080] Among them, the fault probability of each node and branch corresponding to each time can be determined based on the distribution network fault model and the predicted environmental data shown in the preceding embodiments.

[0081] Step S203, according to the fault probability of each node and branch corresponding to each time, determining the fault condition of each node and branch corresponding to each time.

[0082] Specifically, in a feasible embodiment, according to the fault probability of each node and branch corresponding to each time, the fault condition of each node and branch corresponding to each time is determined, including: for each time in the plurality of times, generating a plurality of first uniform random numbers corresponding to the plurality of nodes and a plurality of second uniform random numbers corresponding to the plurality of branches, the first uniform random number and the second uniform random number being between zero and one; when the first uniform random number corresponding to each node in the plurality of nodes is not greater than the fault probability corresponding to each node at each time, it is determined that the fault condition of each node is fault; when the first uniform random number corresponding to each node in the plurality of nodes is greater than the fault probability corresponding to each node at each time, it is determined that the fault condition of each node is not fault; when the second uniform random number corresponding to each branch in the plurality of branches is not greater than the fault probability corresponding to each branch at each time, it is determined that the fault condition of each branch is fault; when the second uniform random number corresponding to each branch in the plurality of branches is greater than the fault probability corresponding to each branch at each time, it is determined that the fault condition of each branch is not fault.

[0083] Among them, in the power system, the fault probability of the plurality of nodes and branches is usually obtained from historical data or empirical statistics. In order to simulate the fault behavior of the entire system under different conditions, the Monte Carlo method can be used to generate the fault scene. The Monte Carlo method can effectively simulate the interaction between multiple random variables (such as node and branch fault probability) through random sampling and statistical analysis.

[0084] First, assume there are N in the system. n Nodes and N b Each branch has an independent probability of failure. and Furthermore, this fault probability is determined based on the distribution network fault model shown in the aforementioned embodiment. Where i = 1, 2, ..., N n j = 1, 2, ..., N b Using the Monte Carlo method, the fault states of each node and branch are first stochastically simulated. For node n... i If the generated random number U i satisfy If the node fails to pass the test, it is considered faulty; otherwise, it is considered normal. Similarly, for branch b... j If the random number V j ≤P bj If so, then the branch circuit is determined to be faulty.

[0085] Repeat step N above. s N times (i.e., N times) s The simulation is conducted in rounds, recording the failure scenarios in each round, including which nodes and branches fail. This simulation data is used to calculate the overall failure probability of the system.

[0086] Node fault diagnosis is represented as follows: Among them, U i It is a uniformly random number on [0,1]. This indicates the fault status of node ni (1 indicates fault, 0 indicates normal).

[0087] Branch circuit fault diagnosis method: Among them, V j It is a uniformly random number on [0,1]. Represents node b j The fault status.

[0088] Total Failure Scenario Matrix: The results of each round of simulation can be represented by a vector. Let be the expression, where t = 1, 2, ..., N. s Through the above steps, a large number of fault scenarios can be generated in the simulation, which can then be used to build a distribution network resilience improvement model.

[0089] Understandably, this requires repeated simulations at each time point, and recording the fault scenarios in each round, including which nodes and branches failed. Finally, the fault scenarios with the greatest impact on the distribution network can be selected. This impact can be determined by the number of node and branch faults; the more node and branch faults, the greater the impact.

[0090] In the present application, the fault scenarios are simulated by the foregoing method, various possible fault combinations can be covered, thereby more facilitating subsequent implementation of resilience improvement of the power distribution network.

[0091] In step S204, a power distribution network resilience improvement model is constructed based on the current operation state of the power distribution network, the fault conditions of the plurality of nodes and the plurality of branches at the plurality of time points respectively, and the target condition constraint.

[0092] The solving target of the power distribution network resilience improvement model includes the maximum value of the sum of the load recovery degrees at the plurality of time points and the minimum value of the switching times of the switch states at the plurality of time points, and the target condition constraint includes the power flow balance constraint.

[0093] In one feasible embodiment, the power distribution network resilience improvement model is represented as:

[0094]

[0095] wherein α and β are used to represent the weights, and the sum of α and β is 1; ω k is used to represent the weight corresponding to node k in the plurality of nodes; y k,t is used to represent the fault condition corresponding to node k at time t, and y k,t is a 0-1 variable, which is 1 when the node is faulty and 0 when the node is not faulty; P k,t is used to represent the load power size corresponding to node k at time t; T is used to represent a preset time period, B is used to represent a set of the plurality of nodes, and E is used to represent a set of the plurality of branches; a l,t is used to represent the fault condition corresponding to branch l at time t, a l,t-1 is used to represent the fault condition corresponding to branch l at time t-1, and similarly a l,t is a 0-1 variable, which is 1 when the branch is faulty and 0 when the branch is not faulty.

[0096] wherein ω k may be set according to the importance of the load at node k. For example, according to the importance of the load at node k, it is divided into 1st, 2nd and 3rd categories, and the weights ω k = ω i (k∈B,i=1,2,3), and ω1≥ω2≥ω3.

[0097] It can be understood that if the initial switch state of branch 1 is closed and branch 1 fails, then branch 1 will switch the switch from closed to open based on fault isolation. If node 1 fails, the load of node 1 cannot be normally supplied, at this time, the switches of the branches connected to the upstream and downstream of node 1 need to be switched to closed, so as to switch the load of node 1 to other normal power supply paths.

[0098] In addition, in one possible implementation, the current operating state of the power distribution network further includes nodes in which distributed power sources are deployed, and the target condition constraint further includes an output power constraint of the distributed power source, which is used to constrain the output power of the distributed power source to be within a preset threshold.

[0099] Further, in one possible implementation, the target condition constraint further includes a closed branch constraint and a single commodity flow constraint. The closed branch constraint is used to constrain the number of closed branches in the plurality of branches to be equal to the number of nodes minus the number of root nodes in the plurality of nodes. The single commodity flow constraint includes a flow constraint, a root node number constraint, and a branch constraint. The flow constraint is used to constrain the sum of inflow power of non-root nodes in the plurality of nodes to be equal to the sum of outflow power plus the load of the node, and / or the sum of inflow power of root nodes in the plurality of nodes to be less than the sum of outflow power plus the load of the node. The root node number constraint is used to constrain the number of root nodes in the plurality of nodes to be less than the number of faulty branches in the plurality of branches plus one. The branch constraint is used to constrain the flow power of faulty branches in the plurality of branches to be zero.

[0100] The target condition constraint is described in detail as follows:

[0101] First, the power distribution network operating constraints, including power flow balance constraints and security constraints, are described as follows:

[0102]

[0103] wherein δ j is the set of branches flowing out of node j, π j is the set of branches flowing into node j, B represents the set of nodes other than substation nodes, and B + represents all nodes in the system. The variables include: node injection power (p, q), branch power flow (P, Q), node voltage (V), and branch current (I). The injection active power p j of node j is equal to the sum of active power generated by generators at node j minus the sum of active loads at node j The injection reactive power q j of node j is equal to the sum of reactive power generated by generators at node j minus the sum of reactive loads at node j P jk represents the active power flowing from node j to node k, P ij represents the active power flowing from node i to node j. Q jk represents the reactive power flowing from node j to node k, Q ij represents the reactive power flowing from node i to node j. rij Yij represents the admittance of the line between node i and node j. ij Gij represents the impedance of the line between node i and node j. j Bj represents the ground impedance at node j. j Yj represents the ground admittance at node j. I ij Imin represents the minimum allowable value of the current between node i and node j. Imax represents the maximum allowable value of the current between node i and node j. V j Vmin represents the minimum allowable value of the voltage at node j. Vmax represents the maximum allowable value of the voltage at node j.

[0104] Let The aforementioned power flow balance constraints and security constraints can be converted into SOCR, which is:

[0105]

[0106] where, I2 represents the square of the current between node i and node j. V2 represents the square of the voltage at node j.

[0107] Secondly, to better achieve the post-disaster recovery of important loads, distributed generators (DGs) are added to some nodes in the proposed model. These DGs are not affected or less affected by disasters and are considered to still work normally during the disaster evolution process. For the DG at node j, its output satisfies:

[0108] where, B DG represents the set of all nodes equipped with distributed generators; the aforementioned formula represents the active power output of the DG at node j at time t and the reactive power output should be between the upper and lower limits of the output. Pmin represents the minimum allowable value of the active power output of the DG at node j. Pmax represents the maximum allowable value of the active power output of the DG at node j. Qmin represents the minimum allowable value of the reactive power output of the DG at node j. Qmax represents the maximum allowable value of the reactive power output of the DG at node j.

[0109] Finally, the distribution network also has a radial topology constraint of the distribution network. The sufficient and necessary condition for meeting the radial constraint is that the connectivity and the node-branch number relationship need to be satisfied simultaneously. This paper proposes a single commodity flow constraint from the perspective of virtual power flow to ensure the connectivity of the graph, which together with the node-branch number relationship constitutes the radial constraint. The formula is as follows:

[0110]

[0111] where, π i and δ i are the set of branches (flow into node i) with tail node i and the set of branches (flow out of node i) with head node i, respectively; N is the number of nodes in the network; R is the set of root nodes in each subnetwork, one root node in each subnetwork, generally a substation node or a distributed power supply node, and the number of root nodes is equal to the number of subnetworks, N R ; F ij represents the virtual flow through the line i→j, F ki represents the virtual flow through the line k→i; D i is the root node flag of each node, which is a 0-1 variable, and if D i = 1, node i is a root node; N fault is the number of fault branches at the current time; M is a large positive real number, generally set as the number of nodes; a ij represents the line opening condition, which is a 0-1 variable, and the line is open for 0 and closed for 1; B is used to represent the set of multiple nodes; and E is used to represent the set of multiple branches.

[0112] The first formula represents that the number of all closed lines is equal to the number of all nodes minus the number of subnetworks, which ensures that each group network generated is a tree. The following three formulas are single commodity flow constraints, which ensure the connectivity of each subnetwork: 1) ensure the virtual flow conservation of each node. Each node has a virtual demand of 1 unit, and the sum of the virtual flows out of all nodes plus the virtual load minus the sum of the virtual flows into the node is the injection power of the node, if the node is not a root node, the injection power is 0, if the node is a root node, the injection power is not 0 and is less than or equal to M; 2) ensure the number of subnetworks. The number of root nodes in the topology is less than the number of fault branches plus one; 3) ensure the connectivity of the branch. The “large M method” is used to judge the line opening condition: if the line is closed, it is equivalent to not making constraints, and if the line is open, the virtual flow of the branch is 0.

[0113] In step S205, the power distribution network resilience improvement model is solved, and the operation state of the power distribution network in the preset time period is determined based on the solving result.

[0114] The power distribution network resilience improvement model includes the multiple constraints shown in the foregoing embodiments, and the optimization variables can further include the branch opening variable a ij , the distributed power supply output variable , the node injection power (p, q), the branch flow (P, Q), the node voltage (V), and the branch current (I). The above model is a mixed integer second-order cone optimization problem, which can be directly solved by using a gurobi solver or other solvers.

[0115] Based on this, the operating status of the distribution network within a preset time period includes the branch on / off status, distributed generation output at each node, node injected power, branch power flow, node voltage, and branch current at each moment. Accordingly, the distribution network can be reconfigured based on its operating status within the preset time period. For example, based on the aforementioned results, the lines and nodes requiring emergency repair can be determined in a timely manner, enabling repair personnel to repair the distribution network nodes and lines.

[0116] As can be seen, in this embodiment, the fault conditions of multiple nodes and branches in the distribution network are first determined based on the wind speed, water depth, and distribution network fault model over a period of time. Then, a distribution network resilience improvement model is constructed based on the fault conditions of multiple nodes and branches over a period of time, the current operating state of the distribution network, and the corresponding objective constraints. The model is solved with the maximum sum of load recovery levels over multiple time periods and the minimum number of switch state switching times over multiple time periods as objectives, yielding the operating state of the distribution network over a period of time. This allows for dynamic reconfiguration of the distribution network based on its operating state over a period of time. This provides a method for quantifying the probability of power grid faults in wind and flood scenarios and proposes a multi-objective, long-term dynamic distribution network reconfiguration scheme to improve the resilience of the distribution network. This scheme can significantly improve the load recovery of the distribution network after a period of disaster evolution, and is particularly suitable for disaster scenarios such as wind and flood disasters that are long-lasting and have complex impacts on the distribution system, thereby improving the resilience of the distribution network against urban flooding disasters.

[0117] For embodiments consistent with those shown above, please refer to... Figure 7 , Figure 7 This is a functional unit block diagram of a power distribution network operation status determination device provided in an embodiment of this application. The power distribution network operation status determination device can be the aforementioned server or a part of a server. Figure 7 As shown, the power distribution network operation status determination device 70 includes:

[0118] The acquisition unit 701 is used to acquire environmental data, a power distribution network fault model, and the current operating status of the power distribution network. The environmental data includes wind speed and water depth at multiple times within a preset time period. The power distribution network fault model is used to predict the fault probability of multiple nodes and multiple branches included in the power distribution network. The current operating status of the power distribution network includes the load power of multiple nodes and the current switching status of multiple branches.

[0119] The processing unit 702 is used to determine the fault probabilities of multiple nodes and multiple branches at multiple times based on environmental data and power distribution network fault models.

[0120] The processing unit 702 is further configured to determine the fault conditions of the plurality of nodes and the plurality of branches at the plurality of time points according to the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of time points respectively.

[0121] The processing unit 702 is further configured to construct a power distribution network resilience improvement model based on the current operation state of the power distribution network, the fault conditions of the plurality of nodes and the plurality of branches at the plurality of time points respectively, and the target condition constraint, the solving target of the power distribution network resilience improvement model including the maximum value of the sum of the load recovery degrees at the plurality of time points and the minimum value of the number of switch state switching at the plurality of time points, and the target condition constraint including the power flow balance constraint.

[0122] The processing unit 702 is further configured to solve the power distribution network resilience improvement model, and determine the operation state of the power distribution network in the preset time period based on the solving result.

[0123] In an available embodiment, in the aspect of determining the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of time points respectively, the processing unit 702 is specifically configured to: when the target parameter is less than a first threshold value, the fault probability is a target probability, the target parameter is a wind speed or a water depth, and the target probability is the fault probability when the node or the branch is not affected by the target parameter; when the target parameter is not less than the first threshold value and less than a second threshold value, the fault probability is determined according to a distribution function of a first value, the first value is determined according to a ratio of a first parameter and a second parameter, the second parameter is a standard deviation of a natural logarithm of the target parameter causing the fault, and the first parameter is a logarithm of a third parameter, the third parameter being a ratio between the target parameter and a median of the target parameter at the fault; and when the target parameter is not less than the second threshold value, the fault probability is one.

[0124] In an available embodiment, the power distribution network fault model includes a tower fault model, an overhead line fault model, and a substation fault model; the tower fault model and the overhead line fault model determine the fault probability according to the wind speed; and the substation fault model determines the fault probability according to the water depth.

[0125] In an implementable embodiment, in terms of determining the fault conditions of the plurality of nodes and the plurality of branches at the plurality of time instants respectively according to the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of time instants respectively, the processing unit 702 is specifically configured to: for each time instant in the plurality of time instants, generate a plurality of first uniform random numbers corresponding to the plurality of nodes respectively, and a plurality of second uniform random numbers corresponding to the plurality of branches respectively, the first uniform random numbers and the second uniform random numbers being between zero and one; when the first uniform random number corresponding to each node in the plurality of nodes is not greater than the fault probability corresponding to each node at each time instant, determine that the fault condition of each node is fault; when the first uniform random number corresponding to each node in the plurality of nodes is greater than the fault probability corresponding to each node at each time instant, determine that the fault condition of each node is non-fault; when the second uniform random number corresponding to each branch in the plurality of branches is not greater than the fault probability corresponding to each branch at each time instant, determine that the fault condition of each branch is fault; when the second uniform random number corresponding to each branch in the plurality of branches is greater than the fault probability corresponding to each branch at each time instant, determine that the fault condition of each branch is non-fault.

[0126] In an implementable embodiment, the power distribution network resilience promotion model is represented as:

[0127]

[0128] wherein α and β are used to represent the weights, and the sum of α and β is 1; ω k is used to represent the weight corresponding to node k in the plurality of nodes; y k,t is used to represent the fault condition corresponding to node k at time instant t, and y k,t is a 0-1 variable, being 1 when the node is fault, and being 0 when the node is non-fault; P k,t is used to represent the load power size corresponding to node k at time instant t; T is used to represent a preset time period, B is used to represent a set of the plurality of nodes, and E is used to represent a set of the plurality of branches; a l,t is used to represent the fault condition corresponding to branch l at time instant t, a l,t-1 is used to represent the fault condition corresponding to branch l at time instant t-1, and similarly a l,t is a 0-1 variable, being 1 when the branch is fault, and being 0 when the branch is non-fault.

[0129] In an implementable embodiment, the current operating state of the power distribution network further includes nodes in the plurality of nodes in which distributed power sources are deployed, and the target condition constraint further includes an output power constraint of the distributed power source, the output power constraint of the distributed power source being used to constrain the output power of the distributed power source to be within a preset threshold.

[0130] In a feasible embodiment, the target condition constraint further comprises a closed branch constraint and a single commodity flow constraint; the closed branch constraint is used to constrain the number of closed branches in the plurality of branches, which is equal to the number of nodes minus the number of root nodes in the plurality of nodes; the single commodity flow constraint comprises a flow constraint, a root node number constraint and a branch constraint; the flow constraint is used to constrain the sum of inflow power of non-root nodes in the plurality of nodes, which is equal to the sum of outflow power plus the load of the node, and / or the sum of inflow power of root nodes in the plurality of nodes, which is less than the sum of outflow power plus the load of the node; the root node number constraint is used to constrain the number of root nodes in the plurality of nodes, which is less than the number of failed branches in the plurality of branches plus one; and the branch constraint is used to constrain the flow power of the failed branches in the plurality of branches to be zero.

[0131] It can be understood that, since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in the present application should be synchronously adapted to the device embodiment part, which will not be described here again.

[0132] In the case of using an integrated unit, as shown in Figure 8 , the device 70 provided by the embodiment of the present application comprises a function unit component block diagram. In Figure 8 , the power distribution network operation state determination device 70 comprises a processing module 812 and a communication module 811. The processing module 812 is used to control and manage the actions of the power distribution network operation state determination device 70, for example, the steps of the acquisition unit 701 and the processing unit 702, and / or other processes for executing the technologies described herein. The communication module 811 is used to support the interaction between the power distribution network operation state determination device 70 and other devices. As shown in Figure 8 , the power distribution network operation state determination device 70 can further comprise a storage module 813, which is used to store the program code and data of the power distribution network operation state determination device 70. Figure 8

[0133] The processing module 812 can be a processor or a controller, for example, a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc. The communication module 811 can be a transceiver, RF circuit or communication interface, etc. The storage module 813 can be a memory. ​

[0134] All the related content of each scenario involved in the above method embodiments can be cited to the function description of the corresponding function module, which will not be repeated here. The above power distribution network operation state determination device 70 can all execute the above Figure 2 The power distribution network operation state determination method shown in the figure.

[0135] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function according to the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (e.g. floppy disk, hard disk, magnetic tape), an optical medium (e.g. DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0136] Figure 9 A structural block diagram of an electronic device provided by the embodiments of the present application is shown. As Figure 9 shown, the electronic device 900 can include one or more of the following components: a processor 901, a memory 902 and a communication interface 903, which are connected to each other and complete the communication work between each other, wherein the memory 902 can store one or more computer programs, and the one or more computer programs can be configured to be executed by the one or more processors 901 to realize the method described in the above embodiments.

[0137] The processor 901 can include one or more processing cores. The processor 901 connects various parts within the entire electronic device 900 by various interfaces and lines, performs various functions of the electronic device 900 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 902, and calling data stored in the memory 902. Optionally, the processor 901 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 901 can integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. It can be understood that the above-mentioned modem can also not be integrated into the processor 901, but can be implemented by a separate communication chip.

[0138] The memory 902 can include a random access memory (RAM) and can also include a read-only memory (ROM). The memory 902 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 902 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area can also store data created by the electronic device 900 in use, etc.

[0139] It can be understood that the electronic device 900 can include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, a physical key, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., which are not limited herein.

[0140] The above-mentioned electronic device 900 can be a server or a part of a server.

[0141] The embodiments of the present application provide a computer readable storage medium, wherein the computer readable storage medium stores program data, and the program data, when executed by a processor, is used to execute part or all steps of any one of the power distribution network operation state determination methods described in the above method embodiments.

[0142] The embodiment of the present application further provides a computer program product comprising a computer program operable to cause a computer to perform some or all of the steps of any of the power distribution network operation state determination method as described in the above method embodiments. The computer program product can be a software installation package.

[0143] It should be noted that, for any of the above power distribution network operation state determination method method embodiments, in order to simply describe, it is expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily necessary for the present application.

[0144] Although the present application is described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and effected by those skilled in the art upon reading the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0145] Those of ordinary skill in the art can understand that all or part of the steps in any of the above power distribution network operation state determination method method embodiments can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable memory, which can include a flash disk, a read-only memory (English: Read-Only Memory, abbreviated: ROM), a random access memory (English: Random Access Memory, abbreviated: RAM), a magnetic disk or an optical disk, etc.

[0146] The above has introduced the embodiments of the present application in detail, and in this paper, specific examples are applied to explain the principles and implementation ways of the power distribution network operation state determination method, device, electronic equipment and storage medium of the present application. The above embodiment description is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the power distribution network operation state determination method, device, electronic equipment and storage medium, the specific implementation and application range will be changed, and according to the above, the content of the specification should not be understood as a limitation of the present application.

[0147] The computer program instructions can 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 block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0148] These computer program instructions can 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 instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0149] The computer program instructions can 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 block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0150] It can be understood that the products, such as the terminal and the computer program product of the above flowcharts, which are controlled or configured to execute the processing method of the flowcharts described in the method embodiments of the power distribution network operation state determination method, all belong to the scope of the related products described in the present application.

[0151] Obviously, persons of ordinary skill in the art can make various modifications and variations to the power distribution network operation state determination method, device, electronic equipment and storage medium provided in the present application without departing from the spirit and scope of the present application. Therefore, if these modifications and variations of the present application fall within the scope of the claims of the present application and the equivalent technologies thereof, the present application also intends to include these modifications and variations.

Claims

1. A method for determining the operating status of a power distribution network, characterized in that, The method includes: The system acquires environmental data, a power distribution network fault model, and the current operating status of the power distribution network. The environmental data includes wind speed and water depth at multiple times within a preset time period. The power distribution network fault model is used to predict the fault probability of multiple nodes and multiple branches in the power distribution network. The current operating status of the power distribution network includes the load power of multiple nodes and the current switching status of multiple branches. Based on the environmental data and the power distribution network fault model, the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of times are determined respectively. Based on the fault probabilities of the multiple nodes and the multiple branches at the multiple times, determine the fault conditions of the multiple nodes and the multiple branches at the multiple times; Based on the current operating state of the distribution network, the fault conditions of the multiple nodes and multiple branches at multiple times, and the target condition constraints, a distribution network elasticity improvement model is constructed. The solution objective of the distribution network elasticity improvement model includes the maximum value of the sum of load recovery degree at multiple times and the minimum value of the number of switch state switching at multiple times. The target condition constraints include power flow balance constraints. Solve the distribution network resilience improvement model, and determine the operating status of the distribution network within the preset time period based on the solution results; The step of determining the fault status of the multiple nodes and multiple branches at multiple times based on their respective fault probabilities at multiple times includes: For each of the multiple time points, generate multiple first uniform random numbers corresponding to multiple nodes and multiple second uniform random numbers corresponding to multiple branches, wherein the first uniform random numbers and the second uniform random numbers are between zero and one; If the first uniform random number corresponding to each of the plurality of nodes is not greater than the fault probability corresponding to each node at each time, then the fault condition of each node is determined to be a fault. If the first uniform random number corresponding to each of the plurality of nodes is greater than the fault probability corresponding to each node at each time, then the fault status of each node is determined to be no fault. If the second uniform random number corresponding to each of the plurality of branches is not greater than the fault probability corresponding to each branch at each time, then the fault condition of each branch is determined to be a fault. If the second uniform random number corresponding to each of the plurality of branches is greater than the fault probability corresponding to each branch at each time, then the fault status of each branch is determined to be no fault. The distribution network resilience enhancement model is expressed as follows: Where α and β represent weights, and the sum of α and β is 1, ω k y is used to represent the weight corresponding to node k among the plurality of nodes. k,t P is used to represent the fault status of node k at time t. k,t The term "a" represents the load power of node k at time t, where T represents the preset time period, B represents the set of nodes, E represents the set of branches, and a l,t a is used to represent the fault status of branch l at time t. l,t-1 Used to indicate the fault status of branch l at time t-1.

2. The method according to claim 1, characterized in that, Determining the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of times includes: When the target parameter is less than the first threshold, the failure probability is the target probability, where the target parameter is the wind speed or the water depth, and the target probability is the failure probability of the node or the branch when it is not affected by the target parameter. When the target parameter is not less than the first threshold and less than the second threshold, the failure probability is determined according to the distribution function of the first value. The first value is determined according to the ratio of the first parameter and the second parameter. The second parameter is the standard deviation of the natural logarithm of the target parameter that caused the failure. The first parameter is the logarithm of the third parameter. The third parameter is the ratio between the target parameter and the median of the target parameter at the time of failure. The probability of failure is one when the target parameter is not less than the second threshold.

3. The method according to claim 1, characterized in that, The power distribution network fault model includes a tower fault model, an overhead line fault model, and a substation fault model; the fault probability of the tower fault model and the overhead line fault model is determined based on the wind speed; the fault probability of the substation fault model is determined based on the water depth.

4. The method according to claim 1, characterized in that, The current operating status of the distribution network also includes nodes among the multiple nodes that have distributed generation resources deployed. The target condition constraints also include the output power constraints of the distributed generation resources, which are used to constrain the output power of the distributed generation resources to be within a preset threshold.

5. The method according to claim 1, characterized in that, The target condition constraints also include closed branch constraints and single commodity flow constraints; The closed branch constraint is used to constrain the number of closed branches in the plurality of branches, which is equal to the number of the plurality of nodes minus the number of root nodes in the plurality of nodes; The single-item flow constraints include flow constraints, root node quantity constraints, and branch constraints. The flow constraint is used to constrain the total inflow power of non-root nodes among the plurality of nodes to be equal to the total outflow power plus the load of that node, and / or the total inflow power of root nodes among the plurality of nodes to be less than the total outflow power plus the load of that node. The root node number constraint is used to ensure that the number of root nodes among the plurality of nodes is less than the number of faulty branches among the plurality of branches plus one. The branch constraint is used to constrain the flow power of the faulty branch among the plurality of branches to zero.

6. A device for determining the operating status of a power distribution network, characterized in that, The device includes: The acquisition unit is used to acquire environmental data, a power distribution network fault model, and the current operating status of the power distribution network. The environmental data includes wind speed and water depth at multiple times within a preset time period. The power distribution network fault model is used to predict the fault probability of multiple nodes and multiple branches included in the power distribution network. The current operating status of the power distribution network includes the load power of multiple nodes and the current switching status of multiple branches. The processing unit is configured to determine the fault probabilities of the plurality of nodes and the plurality of branches at the plurality of times, based on the environmental data and the power distribution network fault model. The processing unit is further configured to determine the fault conditions of the multiple nodes and multiple branches at the multiple times based on the fault probabilities of the multiple nodes and multiple branches at the multiple times. The processing unit is also used to construct a distribution network resilience improvement model based on the current operating state of the distribution network, the fault conditions of the multiple nodes and multiple branches at multiple times, and the target condition constraints. The solution objective of the distribution network resilience improvement model includes the maximum value of the sum of the load recovery degree at multiple times and the minimum value of the number of switch state switching at multiple times. The target condition constraints include power flow balance constraints. The processing unit is also used to solve the distribution network resilience improvement model and determine the operating status of the distribution network within the preset time period based on the solution results; The step of determining the fault status of the multiple nodes and multiple branches at multiple times based on their respective fault probabilities at multiple times includes: For each of the multiple time points, generate multiple first uniform random numbers corresponding to multiple nodes and multiple second uniform random numbers corresponding to multiple branches, wherein the first uniform random numbers and the second uniform random numbers are between zero and one; If the first uniform random number corresponding to each of the plurality of nodes is not greater than the fault probability corresponding to each node at each time, then the fault condition of each node is determined to be a fault. If the first uniform random number corresponding to each of the plurality of nodes is greater than the fault probability corresponding to each node at each time, then the fault status of each node is determined to be no fault. If the second uniform random number corresponding to each of the plurality of branches is not greater than the fault probability corresponding to each branch at each time, then the fault condition of each branch is determined to be a fault. If the second uniform random number corresponding to each of the plurality of branches is greater than the fault probability corresponding to each branch at each time, then the fault status of each branch is determined to be no fault. The distribution network resilience enhancement model is expressed as follows: Where α and β represent weights, and the sum of α and β is 1, ω k y is used to represent the weight corresponding to node k among the plurality of nodes. k,t P is used to represent the fault status of node k at time t. k,t The term "a" represents the load power of node k at time t, where T represents the preset time period, B represents the set of nodes, E represents the set of branches, and a l,t a is used to represent the fault status of branch l at time t. l,t-1 Used to indicate the fault status of branch l at time t-1.

7. An electronic device, the device comprising a processor, a memory, and a computer program stored in the memory, characterized in that, The processor is configured to retrieve a computer program stored in the memory to execute the method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-5.

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